The fastest method for installing this model locally is by using Docker.

Just follow the guidelines provided below.

1-click setup: the app automatically fetches the large weight files.

The installer will automatically analyze your hardware and select the optimal configuration for your system.

🧾 Hash-sum — 2f28f420001e6bda98a03f622a68a4dc • 🗓 Updated on: 2026-06-24 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 100 GB for multi-modal model vision components GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The **chandra-ocr-2** model delivers *state-of-the-art* optical character recognition with unprecedented accuracy across diverse document types. It leverages a deep convolutional neural network architecture combined with attention mechanisms to capture both fine-grained character shapes and contextual layout cues. The model supports a wide range of languages and scripts, making it suitable for global enterprise workflows. Performance benchmarks show a character error rate below 0.5% on standard benchmarks, outperforming previous generations by over 15%. Integration is streamlined via a lightweight API that processes images in *real-time* with minimal hardware requirements.

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