Pin rolm-ocr.py to stable vLLM (>=0.15.1) + datasets>=4.0.0

RolmOCR (Qwen2.5-VL fine-tune) is in stable vLLM. Note: 8B model
needs a100 or larger GPU (OOMs on L4 with default max_model_len=16384).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

rolm-ocr.py

@@ -1,10 +1,10 @@
 # /// script
 # requires-python = ">=3.11"
 # dependencies = [
-#     "datasets",
+#     "datasets>=4.0.0",
 #     "huggingface-hub",
 #     "pillow",
-#     "vllm",
+#     "vllm>=0.15.1",
 #     "tqdm",
 #     "toolz",
 #     "torch",  # Added for CUDA check
@@ -104,7 +104,7 @@ def create_dataset_card(
 ) -> str:
     """Create a dataset card documenting the OCR process."""
     model_name = model.split("/")[-1]
-    
+
     return f"""---
 viewer: false
 tags:
@@ -207,12 +207,13 @@ def main(
     output_column: str = None,
     shuffle: bool = False,
     seed: int = 42,
+    verbose: bool = False,
 ):
     """Process images from HF dataset through OCR model."""
 
     # Check CUDA availability first
     check_cuda_availability()
-    
+
     # Track processing start time
     start_time = datetime.now()
 
@@ -227,13 +228,10 @@ def main(
     # Load dataset
     logger.info(f"Loading dataset: {input_dataset}")
     dataset = load_dataset(input_dataset, split=split)
-    
-    # Set output column name dynamically if not provided
+
+    # Default output column is 'markdown' for consistency across scripts
     if output_column is None:
-        # Extract model name from path (e.g., "reducto/RolmOCR" -> "rolmocr")
-        model_name = model.split("/")[-1].lower().replace("-", "_")
-        output_column = f"{model_name}_text"
-        logger.info(f"Using dynamic output column name: {output_column}")
+        output_column = "markdown"
 
     # Validate image column
     if image_column not in dataset.column_names:
@@ -300,52 +298,53 @@ def main(
     # Add text column to dataset
     logger.info(f"Adding {output_column} column to dataset")
     dataset = dataset.add_column(output_column, all_text)
-    
+
     # Handle inference_info tracking
     logger.info("Updating inference_info...")
-    
-    # Check for existing inference_info
-    if "inference_info" in dataset.column_names:
-        # Parse existing info from first row (all rows have same info)
-        try:
-            existing_info = json.loads(dataset[0]["inference_info"])
-            if not isinstance(existing_info, list):
-                existing_info = [existing_info]  # Convert old format to list
-        except (json.JSONDecodeError, TypeError):
-            existing_info = []
-        # Remove old column to update it
-        dataset = dataset.remove_columns(["inference_info"])
-    else:
-        existing_info = []
-    
-    # Add new inference info
-    new_info = {
-        "column_name": output_column,
+
+    inference_entry = {
         "model_id": model,
-        "processing_date": datetime.now().isoformat(),
+        "model_name": "RolmOCR",
+        "column_name": output_column,
+        "timestamp": datetime.now().isoformat(),
         "batch_size": batch_size,
         "max_tokens": max_tokens,
         "gpu_memory_utilization": gpu_memory_utilization,
         "max_model_len": max_model_len,
         "script": "rolm-ocr.py",
-        "script_version": "1.0.0",
-        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py"
+        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py",
     }
-    existing_info.append(new_info)
-    
-    # Add updated inference_info column
-    info_json = json.dumps(existing_info, ensure_ascii=False)
-    dataset = dataset.add_column("inference_info", [info_json] * len(dataset))
+
+    if "inference_info" in dataset.column_names:
+        logger.info("Updating existing inference_info column")
+
+        def update_inference_info(example):
+            try:
+                existing_info = (
+                    json.loads(example["inference_info"])
+                    if example["inference_info"]
+                    else []
+                )
+            except (json.JSONDecodeError, TypeError):
+                existing_info = []
+            existing_info.append(inference_entry)
+            return {"inference_info": json.dumps(existing_info)}
+
+        dataset = dataset.map(update_inference_info)
+    else:
+        logger.info("Creating new inference_info column")
+        inference_list = [json.dumps([inference_entry])] * len(dataset)
+        dataset = dataset.add_column("inference_info", inference_list)
 
     # Push to hub
     logger.info(f"Pushing to {output_dataset}")
     dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
-    
+
     # Calculate processing time
     end_time = datetime.now()
     processing_duration = end_time - start_time
     processing_time = f"{processing_duration.total_seconds() / 60:.1f} minutes"
-    
+
     # Create and push dataset card
     logger.info("Creating dataset card...")
     card_content = create_dataset_card(
@@ -361,7 +360,7 @@ def main(
         image_column=image_column,
         split=split,
     )
-    
+
     card = DatasetCard(card_content)
     card.push_to_hub(output_dataset, token=HF_TOKEN)
     logger.info("✅ Dataset card created and pushed!")
@@ -371,6 +370,17 @@ def main(
         f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
     )
 
+    if verbose:
+        import importlib.metadata
+
+        logger.info("--- Resolved package versions ---")
+        for pkg in ["vllm", "transformers", "torch", "datasets", "pyarrow", "pillow"]:
+            try:
+                logger.info(f"  {pkg}=={importlib.metadata.version(pkg)}")
+            except importlib.metadata.PackageNotFoundError:
+                logger.info(f"  {pkg}: not installed")
+        logger.info("--- End versions ---")
+
 
 if __name__ == "__main__":
     # Show example usage if no arguments
@@ -396,10 +406,14 @@ if __name__ == "__main__":
         print("\n3. Process a subset for testing:")
         print("   uv run rolm-ocr.py large-dataset test-output --max-samples 10")
         print("\n4. Random sample from ordered dataset:")
-        print("   uv run rolm-ocr.py ordered-dataset random-test --max-samples 50 --shuffle")
+        print(
+            "   uv run rolm-ocr.py ordered-dataset random-test --max-samples 50 --shuffle"
+        )
         print("\n5. Running on HF Jobs:")
         print("   hf jobs uv run --flavor l4x1 \\")
-        print("     -e HF_TOKEN=$(python3 -c \"from huggingface_hub import get_token; print(get_token())\") \\")
+        print(
+            '     -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\'
+        )
         print(
             "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py \\"
         )
@@ -426,7 +440,7 @@ Examples:
   # Random sample of 100 images
   uv run rolm-ocr.py ordered-dataset random-sample --max-samples 100 --shuffle
 
-  # Custom output column name (default: rolmocr_text)
+  # Custom output column name (default: markdown)
   uv run rolm-ocr.py images texts --output-column ocr_text
         """,
     )
@@ -482,7 +496,7 @@ Examples:
     parser.add_argument(
         "--output-column",
         default=None,
-        help="Name of the output column for extracted text (default: auto-generated from model name)",
+        help="Name of the output column for extracted text (default: markdown)",
     )
     parser.add_argument(
         "--shuffle",
@@ -495,6 +509,11 @@ Examples:
         default=42,
         help="Random seed for shuffling (default: 42)",
     )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Log resolved package versions after processing (useful for pinning deps)",
+    )
 
     args = parser.parse_args()
 
@@ -514,4 +533,5 @@ Examples:
         output_column=args.output_column,
         shuffle=args.shuffle,
         seed=args.seed,
-    )
+        verbose=args.verbose,
+    )