DeepSeek-OCR-2 on Your PC

Deploying locally takes the least amount of time when executed through native OS tools.

Please follow the instructions listed below to get started.

The framework seamlessly downloads the massive neural network binaries.

The setup file includes a feature that instantly optimizes all configurations.

📤 Release Hash: 79290eb5d1e02c27521e535898ae5fb5 • 📅 Date: 2026-06-29 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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.

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