DeepSeek released DeepSeek-OCR, a 3-billion-parameter vision-language model for OCR that converts documents to Markdown, parses layouts, and plugs into GPU-accelerated pipelines under a permissive MIT license.
📌 Key Takeaways
- 3B-parameter OCR VLM with BF16 weights and Markdown export.
- Ships under MIT license, enabling broad commercial use.
- Supports multiple presets from Tiny to Large for speed–quality trade-offs.
- Model card references PDF processing via repo guidance and vLLM acceleration.
- Early traction includes 1.6k+ GitHub stars on the project.
What DeepSeek-OCR Is
DeepSeek-OCR is a multimodal model that reads images or pages and returns structured text, including Markdown tables and headings, rather than just raw character streams. It targets real-world documents and screenshots.
The model card frames it as “Contexts Optical Compression,” emphasizing compact, faithful text reconstruction from visual inputs while preserving semantic structure for downstream use.
“Explore the boundaries of visual-text compression.” — DeepSeek AI
Model Sizes, License, And Runtime
The primary checkpoint lists 3B parameters in BF16, suitable for modern consumer and data-center GPUs, with flash-attention support to improve throughput and reduce memory pressure during inference.
Distribution uses Safetensors and MIT licensing, which is friendlier to commercial teams than many research licenses that limit product deployment or fine-tuning.
Presets And Quality–Speed Trade-Offs
Reference usage exposes presets like Tiny, Small, Base, and Large by changing base and image sizes, letting teams align latency and quality with their hardware and SLA needs.
There is also a “Gundam” mode that crops aggressively while keeping a higher base size, useful for dense pages where local detail matters more than full-frame context.
What It Can Extract
Prompts such as “Convert the document to Markdown” yield headings, lists, and tables, which are easier to index, diff, and publish than plain text dumps from legacy OCR engines.
Acknowledgments and example galleries indicate intent to handle charts, tables, forms, and mixed layouts, with community benchmarks like OminiDocBench referenced for evaluation.
Performance Signals And Early Adoption
The model card shows 100+ monthly downloads and a growing community, a typical curve for newly posted research checkpoints on Hugging Face.
The repository itself has accumulated 1.6k+ stars, a soft signal that practitioners are testing it across different OCR and document-AI stacks.
Integrations, Acceleration, And PDFs
The card points developers to the repo for vLLM acceleration guidance, and mentions PDF processing tips, reflecting common pipelines that batch pages and stream Markdown outputs.
That pipeline matters because many enterprise archives are scanned PDFs, where page-wise batching and table retention directly improve analytics and migration tasks.
How To Use DeepSeek-OCR
Below is a concise, reproducible path from install to Markdown output on a single image, aligned with the model card guidance.
- Install deps: Torch 2.6, Transformers 4.46, Flash-Attn 2.7.3, and required Python packages.
- Load model:
AutoModel.from_pretrained("deepseek-ai/DeepSeek-OCR", trust_remote_code=True)with flash_attention_2 and BF16. - Choose preset: Start with Base (1024) for balanced fidelity; try Tiny/Small to cut latency.
- Prompt smartly: Use
<|grounding|> Convert the document to markdown.for structured outputs. - Batch PDFs: Follow repo pointers for vLLM and page batching to accelerate multi-page jobs.
- Save results: Enable
save_results=Trueand write Markdown to your target directory.
This flow balances fidelity and speed while keeping outputs editable and searchable for downstream systems.
Practical Expectations And Limits
As a compact VLM, DeepSeek-OCR trades peak accuracy for speed and accessibility, making presets important when pages include handwriting, stamps, or low-contrast scans.
For heavy PDF archives, acceleration via vLLM and careful crop strategies typically matters more than raw parameter count, especially on mid-range GPUs.
Conclusion
DeepSeek-OCR brings permissive licensing, Markdown-first exports, and GPU-friendly presets to document AI, lowering the barrier to structured extraction without brittle rule-based post-processing.
Teams gain a practical stepping stone for searchable archives, analytics pipelines, and LLM-ready context building, with room to scale into PDF batches and faster runtimes.
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20th October 2025
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