Add hf-jobs tag to README frontmatter

Standardizing metadata tags across uv-scripts organization
for better discoverability of HF Jobs-compatible scripts.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

README.md

@@ -31,6 +31,54 @@ That's it! The script will:
 
 ## 📋 Available Scripts
 
+### PaddleOCR-VL-1.5 (`paddleocr-vl-1.5.py`) 🏆 SOTA with 6 task modes!
+
+Ultra-compact SOTA OCR using [PaddlePaddle/PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) with 94.5% accuracy:
+
+- 🏆 **SOTA Performance** - 94.5% on OmniDocBench v1.5
+- 🧩 **Ultra-compact** - Only 0.9B parameters
+- 📝 **OCR mode** - General text extraction to markdown
+- 📊 **Table mode** - HTML table recognition
+- 📐 **Formula mode** - LaTeX mathematical notation
+- 📈 **Chart mode** - Chart and diagram analysis
+- 🔍 **Spotting mode** - Text spotting with localization (higher resolution)
+- 🔖 **Seal mode** - Seal and stamp recognition
+- 🌍 **Multilingual** - Support for multiple languages
+
+**Task Modes:**
+
+- `ocr`: General text extraction (default)
+- `table`: Table extraction to HTML
+- `formula`: Mathematical formula to LaTeX
+- `chart`: Chart and diagram analysis
+- `spotting`: Text spotting with localization
+- `seal`: Seal and stamp recognition
+
+**Quick start:**
+
+```bash
+# Basic OCR mode
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
+    your-input-dataset your-output-dataset \
+    --max-samples 100
+
+# Table extraction
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
+    documents tables-extracted \
+    --task-mode table
+
+# Seal recognition
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
+    documents seals-extracted \
+    --task-mode seal
+```
+
 ### PaddleOCR-VL (`paddleocr-vl.py`) 🎯 Smallest model with task-specific modes!
 
 Ultra-compact OCR using [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) with only 0.9B parameters:
@@ -93,21 +141,21 @@ Compact high-performance OCR using [zai-org/GLM-OCR](https://huggingface.co/zai-
 
 ```bash
 # Basic OCR
-hf jobs uv run --flavor a100-large \
+hf jobs uv run --flavor l4x1 \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     your-input-dataset your-output-dataset \
     --max-samples 100
 
 # Formula recognition
-hf jobs uv run --flavor a100-large \
+hf jobs uv run --flavor l4x1 \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     scientific-papers formulas-extracted \
     --task formula
 
 # Table extraction
-hf jobs uv run --flavor a100-large \
+hf jobs uv run --flavor l4x1 \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     documents tables-extracted \
@@ -364,7 +412,7 @@ hf jobs uv run --flavor l4x1 \
     --batch-size 32
 
 # GLM-OCR - SOTA 0.9B model (94.62% OmniDocBench)
-hf jobs uv run --flavor a100-large \
+hf jobs uv run --flavor l4x1 \
     -s HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
     your-input-dataset your-output-dataset \
@@ -527,6 +575,13 @@ Any HuggingFace dataset containing images - documents, forms, receipts, books, h
 
 ### Script-Specific Options
 
+**PaddleOCR-VL-1.5**:
+
+- `--task-mode`: Task type - `ocr` (default), `table`, `formula`, `chart`, `spotting`, or `seal`
+- `--output-column`: Override default column name (default: `paddleocr_1.5_[task_mode]`)
+- SOTA 94.5% accuracy on OmniDocBench v1.5
+- Uses transformers backend (single image processing for stability)
+
 **PaddleOCR-VL**:
 
 - `--task-mode`: Task type - `ocr` (default), `table`, `formula`, or `chart`
@@ -539,7 +594,7 @@ Any HuggingFace dataset containing images - documents, forms, receipts, books, h
 - `--task`: Task type - `ocr` (default), `formula`, or `table`
 - `--repetition-penalty`: Repetition penalty (default: 1.1, from official SDK)
 - Near-greedy sampling by default (temperature=0.01, top_p=0.00001) matching official SDK
-- Requires vLLM nightly + transformers from git (handled automatically)
+- Requires vLLM nightly + transformers>=5.1.0 (handled automatically)
 
 **DeepSeek-OCR**: