Running this model locally is fastest when deployed through Docker.
Make sure to follow the instructions below.
You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.
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% |
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