Simplify nanonets-ocr2.py to 3B-only and fix dataset card

- Remove 1.5B model support (vLLM compatibility issues)
- Focus on working Nanonets-OCR2-3B model (3.75B params)
- Remove viewer:false from dataset card (allow dataset viewer)
- Set batch size default to 16 (appropriate for 3B model)
- Clean up documentation and help text

🤖 Generated with Claude Code

nanonets-ocr2.py

@@ -13,14 +13,10 @@
 # ///
 
 """
-Convert document images to markdown using Nanonets-OCR2 models with vLLM.
+Convert document images to markdown using Nanonets-OCR2-3B with vLLM.
 
-This script processes images through Nanonets-OCR2 models (1.5B or 3B) to extract
-text and structure as markdown, ideal for document understanding tasks.
-
-Models:
-- Nanonets-OCR2-3B (default): 3.75B params, best quality
-- Nanonets-OCR2-1.5B-exp: 1.65B params, faster processing
+This script processes images through the Nanonets-OCR2-3B model (3.75B params)
+to extract text and structure as markdown, ideal for document understanding tasks.
 
 Features:
 - LaTeX equation recognition
@@ -110,10 +106,8 @@ def create_dataset_card(
 ) -> str:
     """Create a dataset card documenting the OCR process."""
     model_name = model.split("/")[-1]
-    model_size = "3B" if "3B" in model else "1.5B"
 
     return f"""---
-viewer: false
 tags:
 - ocr
 - document-processing
@@ -126,13 +120,13 @@ tags:
 
 # Document OCR using {model_name}
 
-This dataset contains markdown-formatted OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using Nanonets-OCR2-{model_size}.
+This dataset contains markdown-formatted OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using Nanonets-OCR2-3B.
 
 ## Processing Details
 
 - **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
 - **Model**: [{model}](https://huggingface.co/{model})
-- **Model Size**: {model_size} parameters
+- **Model Size**: 3.75B parameters
 - **Number of Samples**: {num_samples:,}
 - **Processing Time**: {processing_time}
 - **Processing Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
@@ -149,7 +143,7 @@ This dataset contains markdown-formatted OCR results from images in [{source_dat
 
 ## Model Information
 
-Nanonets-OCR2-{model_size} is a state-of-the-art document OCR model that excels at:
+Nanonets-OCR2-3B is a state-of-the-art document OCR model that excels at:
 - 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
 - 📊 **Tables** - Extracted and formatted as HTML
 - 📝 **Document structure** - Headers, lists, and formatting maintained
@@ -214,7 +208,7 @@ def main(
     input_dataset: str,
     output_dataset: str,
     image_column: str = "image",
-    batch_size: int = None,
+    batch_size: int = 16,
     model: str = "nanonets/Nanonets-OCR2-3B",
     max_model_len: int = 8192,
     max_tokens: int = 4096,
@@ -226,16 +220,7 @@ def main(
     shuffle: bool = False,
     seed: int = 42,
 ):
-    """Process images from HF dataset through Nanonets-OCR2 model."""
-
-    # Auto-set batch size based on model if not specified
-    if batch_size is None:
-        if "1.5B" in model:
-            batch_size = 32
-            logger.info("Auto-set batch size to 32 for 1.5B model")
-        else:  # 3B model
-            batch_size = 16
-            logger.info("Auto-set batch size to 16 for 3B model")
+    """Process images from HF dataset through Nanonets-OCR2-3B model."""
 
     # Check CUDA availability first
     check_cuda_availability()
@@ -395,13 +380,10 @@ if __name__ == "__main__":
     # Show example usage if no arguments
     if len(sys.argv) == 1:
         print("=" * 80)
-        print("Nanonets OCR2 to Markdown Converter")
+        print("Nanonets OCR2-3B to Markdown Converter")
         print("=" * 80)
         print("\nThis script converts document images to structured markdown using")
-        print("Nanonets-OCR2 models (1.5B or 3B) with vLLM acceleration.")
-        print("\nModel Options:")
-        print("- Nanonets-OCR2-3B (default): 3.75B params, best quality")
-        print("- Nanonets-OCR2-1.5B-exp: 1.65B params, faster processing")
+        print("the Nanonets-OCR2-3B model (3.75B params) with vLLM acceleration.")
         print("\nFeatures:")
         print("- LaTeX equation recognition")
         print("- Table extraction and formatting (HTML)")
@@ -411,22 +393,19 @@ if __name__ == "__main__":
         print("- Checkbox recognition (☐/☑)")
         print("- Multilingual support")
         print("\nExample usage:")
-        print("\n1. Basic OCR conversion (3B model, best quality):")
+        print("\n1. Basic OCR conversion:")
         print("   uv run nanonets-ocr2.py document-images markdown-docs")
-        print("\n2. Fast processing with 1.5B model:")
-        print("   uv run nanonets-ocr2.py documents output \\")
-        print("       --model nanonets/Nanonets-OCR2-1.5B-exp")
-        print("\n3. With custom settings:")
+        print("\n2. With custom settings:")
         print("   uv run nanonets-ocr2.py scanned-pdfs extracted-text \\")
         print("       --image-column page \\")
         print("       --batch-size 32 \\")
         print("       --gpu-memory-utilization 0.8")
-        print("\n4. Process a subset for testing:")
+        print("\n3. Process a subset for testing:")
         print("   uv run nanonets-ocr2.py large-dataset test-output --max-samples 10")
-        print("\n5. Random sample from ordered dataset:")
+        print("\n4. Random sample from ordered dataset:")
         print("   uv run nanonets-ocr2.py ordered-dataset random-test \\")
         print("       --max-samples 50 --shuffle")
-        print("\n6. Running on HF Jobs:")
+        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("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \\")
@@ -437,20 +416,13 @@ if __name__ == "__main__":
         sys.exit(0)
 
     parser = argparse.ArgumentParser(
-        description="OCR images to markdown using Nanonets-OCR2 models",
+        description="OCR images to markdown using Nanonets-OCR2-3B",
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
-Models:
-  nanonets/Nanonets-OCR2-3B (default)      - 3.75B params, best quality
-  nanonets/Nanonets-OCR2-1.5B-exp          - 1.65B params, faster
-
 Examples:
-  # Basic usage (3B model)
+  # Basic usage
   uv run nanonets-ocr2.py my-images-dataset ocr-results
 
-  # Fast processing with 1.5B model
-  uv run nanonets-ocr2.py documents output --model nanonets/Nanonets-OCR2-1.5B-exp
-
   # With specific image column
   uv run nanonets-ocr2.py documents extracted-text --image-column scan
 
@@ -472,14 +444,13 @@ Examples:
     parser.add_argument(
         "--batch-size",
         type=int,
-        default=None,
-        help="Batch size for processing (default: auto - 16 for 3B, 32 for 1.5B)",
+        default=16,
+        help="Batch size for processing (default: 16)",
     )
     parser.add_argument(
         "--model",
         default="nanonets/Nanonets-OCR2-3B",
-        choices=["nanonets/Nanonets-OCR2-3B", "nanonets/Nanonets-OCR2-1.5B-exp"],
-        help="Model to use (default: Nanonets-OCR2-3B for best quality)",
+        help="Model to use (default: nanonets/Nanonets-OCR2-3B)",
     )
     parser.add_argument(
         "--max-model-len",