Zero-Click Run olmOCR-2-7B-1025-FP8 Using Pinokio Quantized GGUF Full Method

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🛠 Hash code: 7099fd9d69e6c588c2a26ba4f1c02d84 — Last modification: 2026-07-03

Processor: 6-core 3.5 GHz minimum required
RAM: 48 GB needed to prevent memory swapping to disk
Disk Space:70 GB free space for full FP16 weights storage
GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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