Update nanonets-ocr.py HF Jobs syntax and add smoke test notes

- Replace deprecated `hfjobs run` with current `hf jobs uv run` syntax
- Add OCR smoke test dataset design notes to CLAUDE.md

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

CLAUDE.md

@@ -9,32 +9,27 @@
 - Tested and working on HF Jobs
 
 ### LightOnOCR-2-1B (`lighton-ocr2.py`)
-⚠️ **Temporarily Broken** (2026-01-29)
+✅ **Production Ready** (Fixed 2026-01-29)
 
-**Status:** vLLM nightly regression - image processor loading fails
+**Status:** Working with vLLM nightly
 
-**What happened:**
-- Script was working with vLLM nightly `v0.15.0rc2.dev73`
-- Nightly updated to `v0.15.0rc2.dev81` and broke
-- Error: `OSError: Can't load image processor for 'lightonai/LightOnOCR-2-1B'`
-- Both nightly and stable vLLM 0.14.x have this issue now
+**What was fixed:**
+- Root cause was NOT vLLM - it was the deprecated `HF_HUB_ENABLE_HF_TRANSFER=1` env var
+- The script was setting this env var but `hf_transfer` package no longer exists
+- This caused download failures that manifested as "Can't load image processor" errors
+- Fix: Removed the `HF_HUB_ENABLE_HF_TRANSFER=1` setting from the script
 
-**Initial test results (before breakage):**
-- 8/10 samples had good OCR output
-- 2/10 samples showed repetition loops (likely due to max_tokens=6144)
-- Changed max_tokens default from 6144 → 4096 (per model card recommendation)
+**Test results (2026-01-29):**
+- 10/10 samples processed successfully
+- Clean markdown output with proper headers and paragraphs
+- Output dataset: `davanstrien/lighton-ocr2-test-v4`
 
-**Fixes applied:**
-- `max_tokens`: 6144 → 4096 (model card recommends 4096 for arXiv papers)
-- Fixed pyarrow compatibility (>=17.0.0,<18.0.0)
-- Replaced deprecated `huggingface-hub[hf_transfer]` with `hf-xet`
-
-**To verify when vLLM is fixed:**
+**Example usage:**
 ```bash
 hf jobs uv run --flavor a100-large \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
-    davanstrien/ufo-ColPali davanstrien/lighton-ocr2-test-v3 \
+    davanstrien/ufo-ColPali output-dataset \
     --max-samples 10 --shuffle --seed 42
 ```
 
@@ -44,6 +39,42 @@ hf jobs uv run --flavor a100-large \
 - Training: RLVR (Reinforcement Learning with Verifiable Rewards)
 - Performance: 83.2% on OlmOCR-Bench, 42.8 pages/sec on H100
 
+### PaddleOCR-VL-1.5 (`paddleocr-vl-1.5.py`)
+✅ **Production Ready** (Added 2026-01-30)
+
+**Status:** Working with transformers
+
+**Note:** Uses transformers backend (not vLLM) because PaddleOCR-VL only supports vLLM in server mode, which doesn't fit the single-command UV script pattern. Images are processed one at a time for stability.
+
+**Test results (2026-01-30):**
+- 10/10 samples processed successfully
+- Processing time: ~50s per image on L4 GPU
+- Output dataset: `davanstrien/paddleocr-vl15-final-test`
+
+**Example usage:**
+```bash
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
+    davanstrien/ufo-ColPali output-dataset \
+    --max-samples 10 --shuffle --seed 42
+```
+
+**Task modes:**
+- `ocr` (default): General text extraction to markdown
+- `table`: Table extraction to HTML format
+- `formula`: Mathematical formula recognition to LaTeX
+- `chart`: Chart and diagram analysis
+- `spotting`: Text spotting with localization (uses higher resolution)
+- `seal`: Seal and stamp recognition
+
+**Model Info:**
+- Model: `PaddlePaddle/PaddleOCR-VL-1.5`
+- Size: 0.9B parameters (ultra-compact)
+- Performance: 94.5% SOTA on OmniDocBench v1.5
+- Backend: Transformers (single image processing)
+- Requires: `transformers>=5.0.0`
+
 ## Pending Development
 
 ### DeepSeek-OCR-2 (Visual Causal Flow Architecture)
@@ -139,7 +170,44 @@ RESOLUTION_MODES = {
 
 ---
 
-**Last Updated:** 2026-01-29
+## Future: OCR Smoke Test Dataset
+
+**Status:** Idea (noted 2026-02-12)
+
+Build a small curated dataset (`uv-scripts/ocr-smoke-test`?) with ~2-5 samples from diverse sources. Purpose: fast CI-style verification that scripts still work after dep updates, without downloading full datasets.
+
+**Design goals:**
+- Tiny (~20-30 images total) so download is seconds not minutes
+- Covers the axes that break things: document type, image quality, language, layout complexity
+- Has ground truth text where possible for quality regression checks
+- All permissively licensed (CC0/CC-BY preferred)
+
+**Candidate sources:**
+
+| Source | What it covers | Why |
+|--------|---------------|-----|
+| `NationalLibraryOfScotland/medical-history-of-british-india` | Historical English, degraded scans | Has hand-corrected `text` column for comparison. CC0. Already tested with GLM-OCR. |
+| `davanstrien/ufo-ColPali` | Mixed modern documents | Already used as our go-to test set. Varied layouts. |
+| Something with **tables** | Structured data extraction | Tests `--task table` modes. Maybe a financial report or census page. |
+| Something with **formulas/LaTeX** | Math notation | Tests `--task formula`. arXiv pages or textbook scans. |
+| Something **multilingual** (CJK, Arabic, etc.) | Non-Latin scripts | GLM-OCR claims zh/ja/ko support. Good to verify. |
+| Something **handwritten** | Handwriting recognition | Edge case that reveals model limits. |
+
+**How it would work:**
+```bash
+# Quick smoke test for any script
+uv run glm-ocr.py uv-scripts/ocr-smoke-test smoke-out --max-samples 5
+# Or a dedicated test runner that checks all scripts against it
+```
+
+**Open questions:**
+- Build as a proper HF dataset, or just a folder of images in the repo?
+- Should we include expected output for regression testing (fragile if models change)?
+- Could we add a `--smoke-test` flag to each script that auto-uses this dataset?
+- Worth adding to HF Jobs scheduled runs for ongoing monitoring?
+
+---
+
+**Last Updated:** 2026-02-12
 **Watch PRs:**
 - DeepSeek-OCR-2: https://github.com/vllm-project/vllm/pull/33165
-- LightOnOCR-2 regression: Check https://github.com/vllm-project/vllm/issues?q=LightOnOCR

nanonets-ocr.py

@@ -103,7 +103,7 @@ def create_dataset_card(
 ) -> str:
     """Create a dataset card documenting the OCR process."""
     model_name = model.split("/")[-1]
-    
+
     return f"""---
 viewer: false
 tags:
@@ -218,7 +218,7 @@ def main(
 
     # Check CUDA availability first
     check_cuda_availability()
-    
+
     # Track processing start time
     start_time = datetime.now()
 
@@ -299,10 +299,10 @@ def main(
     # Add markdown column to dataset
     logger.info("Adding markdown column to dataset")
     dataset = dataset.add_column("markdown", all_markdown)
-    
+
     # 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)
@@ -316,7 +316,7 @@ def main(
         dataset = dataset.remove_columns(["inference_info"])
     else:
         existing_info = []
-    
+
     # Add new inference info
     new_info = {
         "column_name": "markdown",
@@ -328,10 +328,10 @@ def main(
         "max_model_len": max_model_len,
         "script": "nanonets-ocr.py",
         "script_version": "1.0.0",
-        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py"
+        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-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))
@@ -339,12 +339,12 @@ def main(
     # 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(
@@ -359,7 +359,7 @@ 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!")
@@ -394,13 +394,14 @@ if __name__ == "__main__":
         print("\n3. Process a subset for testing:")
         print("   uv run nanonets-ocr.py large-dataset test-output --max-samples 10")
         print("\n4. Random sample from ordered dataset:")
-        print("   uv run nanonets-ocr.py ordered-dataset random-test --max-samples 50 --shuffle")
+        print(
+            "   uv run nanonets-ocr.py ordered-dataset random-test --max-samples 50 --shuffle"
+        )
         print("\n5. Running on HF Jobs:")
-        print("   hfjobs run \\")
-        print("     --flavor l4x1 \\")
-        print("     --secret HF_TOKEN=... \\")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print("     -s HF_TOKEN \\")
         print(
-            "     uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \\"
+            "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \\"
         )
         print("       your-document-dataset \\")
         print("       your-markdown-output")