To install this model locally in the shortest time, opt for a direct curl execution.
Check out the detailed setup guide below to begin.
1-click setup: the app automatically fetches the large weight files.
To save you time, the system will automatically determine efficient resource allocation.
The DeepSeek-OCR-2 model sets a new benchmark in document understanding by combining highāresolution image processing with a novel attention mechanism that captures contextual relationships across lines and paragraphs. Its architecture leverages a multiāscale convolutional backbone, enabling robust performance on both printed and handwritten scripts while maintaining fast inference speeds on standard GPUs. A dedicated languageāagnostic tokenizer expands the model’s vocabulary to over 200āÆk subword units, supporting more than 100 languages and specialized domain terminologies. In comparative benchmarks, DeepSeek-OCR-2 achieves an average accuracy of 98.7āÆ% on the DocVQA dataset, surpassing the previous stateāofātheāart by a margin of 1.4āÆ%. The accompanying openāsource toolkit provides preātrained checkpoints, data augmentation pipelines, and a simple API, allowing developers to fineātune 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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