A standalone PowerShell module provides the fastest route to local installation.
Please follow the instructions listed below to get started.
All large files and heavy weights are downloaded automatically by the script.
The installer diagnoses your environment to deploy the most compatible profile.
The DeepSeek-OCR-2 model sets a new benchmark in document understanding by combining high‑resolution image processing with a novel attention mechanism that captures contextual relationships across lines and paragraphs. Its architecture leverages a multi‑scale convolutional backbone, enabling robust performance on both printed and handwritten scripts while maintaining fast inference speeds on standard GPUs. A dedicated language‑agnostic tokenizer expands the model’s vocabulary to over 200 k subword units, supporting more than 100 languages and specialized domain terminologies. In comparative benchmarks, DeepSeek-OCR-2 achieves an average accuracy of 98.7 % on the DocVQA dataset, surpassing the previous state‑of‑the‑art by a margin of 1.4 %. The accompanying open‑source toolkit provides pre‑trained checkpoints, data augmentation pipelines, and a simple API, allowing developers to fine‑tune the model for custom OCR pipelines with minimal overhead.
| Model name | DeepSeek-OCR-2 |
| Parameters | 1.2B |
| Input resolution | 1024×1024 |
| Supported languages | 100 |
| Accuracy (DocVQA) | 98.7% |
- Setup utility automating prompt cache reuse for faster generations
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- Installer deploying complex ComfyUI nodes for Flux-ControlNet-Inpainting workflows
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- Installer deploying offline face recovery modules alongside pre-trained weight array builds
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- Setup utility configuring Amuse software for offline image generation via ROCm backends
- How to Autostart DeepSeek-OCR-2
- Script automating download of Stable Diffusion 3.5 Turbo weights directly to nvme storage nodes
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