Document DeepSeek-OCR in README with real examples

Added comprehensive documentation for deepseek-ocr-vllm.py:
- Added to Available Scripts section with ⭐ NEW badge
- Documented all 5 resolution modes (tiny/small/base/large/gundam)
- Documented all 5 prompt modes (document/image/free/figure/describe)
- Added 3 real-world HF Jobs examples:
* National Library of Scotland handbooks (a100-large, large mode)
* Fast testing with tiny mode (l4x1)
* Figure parsing from scientific papers
- Added script-specific options section
- Emphasized UV_TORCH_BACKEND=auto requirement for HF Jobs

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

README.md

@@ -31,6 +31,34 @@ That's it! The script will:
 
 ## 📋 Available Scripts
 
+### DeepSeek-OCR (`deepseek-ocr-vllm.py`) ⭐ NEW
+
+Advanced document OCR using [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) with visual-text compression:
+
+- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format
+- 📊 **Tables** - Extracted as HTML/markdown
+- 📝 **Document structure** - Headers, lists, formatting preserved
+- 🖼️ **Image grounding** - Spatial layout with bounding boxes
+- 🔍 **Complex layouts** - Multi-column and hierarchical structures
+- 🌍 **Multilingual** - Multiple language support
+- 🎚️ **Resolution modes** - 5 presets for speed/quality trade-offs
+- 💬 **Prompt modes** - 5 presets for different OCR tasks
+- ⚡ **Fast batch processing** - vLLM acceleration
+
+**Resolution Modes:**
+- `tiny` (512×512): Fast, 64 vision tokens
+- `small` (640×640): Balanced, 100 vision tokens
+- `base` (1024×1024): High quality, 256 vision tokens
+- `large` (1280×1280): Maximum quality, 400 vision tokens
+- `gundam` (dynamic): Adaptive multi-tile (default)
+
+**Prompt Modes:**
+- `document`: Convert to markdown with grounding (default)
+- `image`: OCR any image with grounding
+- `free`: Fast OCR without layout
+- `figure`: Parse figures from documents
+- `describe`: Detailed image descriptions
+
 ### RolmOCR (`rolm-ocr.py`)
 
 Fast general-purpose OCR using [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR) based on Qwen2.5-VL-7B:
@@ -141,7 +169,35 @@ Every OCR run now generates comprehensive dataset documentation including:
 No GPU? No problem! Run on HF infrastructure:
 
 ```bash
-# Basic OCR job
+# DeepSeek-OCR - Real-world example (National Library of Scotland handbooks)
+hf jobs uv run --flavor a100-large \
+    -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=auto \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
+    NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \
+    davanstrien/handbooks-deep-ocr \
+    --max-samples 100 \
+    --shuffle \
+    --resolution-mode large
+
+# DeepSeek-OCR - Fast testing with tiny mode
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=auto \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
+    your-input-dataset your-output-dataset \
+    --max-samples 10 \
+    --resolution-mode tiny
+
+# DeepSeek-OCR - Parse figures from scientific papers
+hf jobs uv run --flavor a100-large \
+    -s HF_TOKEN \
+    -e UV_TORCH_BACKEND=auto \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \
+    scientific-papers figures-extracted \
+    --prompt-mode figure
+
+# Basic OCR job with Nanonets
 hf jobs uv run --flavor l4x1 \
     --secrets HF_TOKEN \
     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
@@ -255,6 +311,13 @@ Any HuggingFace dataset containing images - documents, forms, receipts, books, h
 
 ### Script-Specific Options
 
+**DeepSeek-OCR**:
+- `--resolution-mode`: Quality level - `tiny`, `small`, `base`, `large`, or `gundam` (default)
+- `--prompt-mode`: Task type - `document` (default), `image`, `free`, `figure`, or `describe`
+- `--prompt`: Custom OCR prompt (overrides prompt-mode)
+- `--base-size`, `--image-size`, `--crop-mode`: Override resolution mode manually
+- ⚠️ **Important for HF Jobs**: Add `-e UV_TORCH_BACKEND=auto` for proper PyTorch installation
+
 **RolmOCR**:
 - Output column is auto-generated from model name (e.g., `rolmocr_text`)
 - Use `--output-column` to override the default name