- Allow users to specify custom output column name
- Default remains "markdown" for consistency
- Matches pattern from dots-ocr.py and rolm-ocr.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Change olmOCR2 temperature from 0.1 to 0.0 to match original implementation
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>
This ensures UV automatically selects the correct PyTorch wheel
based on the available CUDA/GPU environment during installation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Changed from:
[[tool.uv.index]]
name = "vllm-nightly"
url = "https://wheels.vllm.ai"
To:
[[tool.uv.index]]
url = "https://wheels.vllm.ai/nightly"
- Removed unnecessary 'name' field (simpler syntax per UV docs)
- Fixed URL to use /nightly path for nightly builds
- Should now properly install pre-built wheels instead of building from source
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
PR #27247 is now merged to main, so we can use the official nightly wheels
instead of the fork branch.
Changes:
- Replace git+https://github.com/Isotr0py/vllm.git@deepseek-ocr with vllm
- Add [[tool.uv.index]] for https://wheels.vllm.ai
- Keep prerelease = "allow" for nightly builds
- Update docstrings to reflect PR merged status
- Update error messages (remove PR reference)
This should provide faster installation since we're using pre-built wheels
instead of compiling from source.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add temp_output directory and pass real path instead of empty string:
- Create temp_output/ directory
- Pass to model.infer as output_path parameter
- Clean up both temp directories at end
The model expects a real directory path, not empty string.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Use local temp_images directory instead of system temp
Replace tempfile.mkdtemp() with simple local directory:
- Create "temp_images" in current working directory
- More reliable in containerized environments like HF Jobs
- Simpler and easier to debug
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Replace NamedTemporaryFile with simple temp directory approach:
- Create temp dir once at start
- Reuse single temp file path for all images
- Clean up temp dir at end
This matches the official example pattern where model.infer()
expects a real file path string.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Pin transformers version for DeepSeek-OCR compatibility
Fix ImportError with LlamaFlashAttention2 by pinning to
transformers==4.46.3 and tokenizers==0.20.3 as required
by the model's custom code.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Initial implementation using official transformers API.
- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)
- Sequential processing (no batching)
- Full dataset processing support
- Dataset card generation
- Compatible with HF Jobs
To test before updating README.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add Nanonets OCR2 (nanonets-ocr2.py) to available scripts section
- Highlight 3.75B params, enhanced quality, and Qwen2.5-VL base
- Add HF Jobs example for Nanonets OCR2
- Add local usage example
- Position as next-generation quality option
🤖 Generated with Claude Code
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
Add Nanonets-OCR2 script with dual model support (1.5B/3B)
- Supports both Nanonets-OCR2-3B (3.75B params, best quality) and Nanonets-OCR2-1.5B-exp (1.65B params, faster)
- Auto-adjusts batch size based on model selection (16 for 3B, 32 for 1.5B)
- Same prompts and capabilities across both models
- LaTeX equations, HTML tables, image captions, watermarks, checkboxes
- Multilingual support
🤖 Generated with Claude Code
Add DoTS.ocr to documentation with official --secrets syntax
- Add rolm-ocr.py: Fast general-purpose OCR using reducto/RolmOCR
- Add inference_info tracking to all OCR scripts for multi-model comparison
- Add --shuffle and --seed flags for random sampling
- Add automatic dataset card generation with processing details
- Update README.md with new script documentation and feature descriptions
- Dynamic column naming for RolmOCR (e.g., rolmocr_text)
These enhancements support iterative model comparison workflows and better sampling strategies for OCR evaluation.
Optimize default settings based on performance testing
- Increase default batch size from 8 to 32
- Increase default GPU memory utilization from 0.7 to 0.8
- Update README with new defaults and simple performance tip
- These changes provide ~2-3x speedup based on testing
- UV script for document OCR using Nanonets-OCR-s model
- Features: LaTeX equations, tables, document structure
- Supports batch processing with vLLM
- Includes HF Jobs examples for running on cloud
- Added proper CUDA checks and error handling
