by 
If you need a near-instant local setup, just fetch files via a basic curl request.
Kindly follow the on-screen instructions below.
Be patient as the system self-retrieves massive model weights dynamically.
An automated hardware sweep ensures the system will select the best tuning parameters.
olmOCR-2-7B-1025-FP8 delivers state‑of‑the‑art optical character recognition with a massive 7‑billion parameter base, enabling unprecedented accuracy on complex document layouts. Built on the FP8 quantization scheme, it achieves a balanced trade‑off between inference speed and memory footprint, making it suitable for both cloud and edge deployments. The architecture incorporates a refined vision encoder that processes high‑resolution scans up to 1025 × 1025 pixels, preserving fine glyphs and contextual spacing. A dedicated language model head leverages multilingual tokenizers, supporting over 100 languages while maintaining a low error rate on cursive and printed text. Benchmark results show a 3.2 % absolute gain over the previous generation on the PubLayNet dataset, and the model is openly released under an permissive license for research and commercial use.
| Model | olmOCR-2-7B-1025-FP8 |
| Parameters | 7 B |
| Input Resolution | 1025 × 1025 |
| Quantization | FP8 |
| Supported Languages | 100+ |
| License | Permissive (Apache 2.0) |
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