Setup chandra-ocr-2 with 1M Context

The fastest way to get this model running locally is via Docker.

Simply follow the directions outlined below.

No manual effort needed; the setup auto-ingests the large data.

The automated installation script takes care of everything by tailoring the setup perfectly to your system specs.

🧮 Hash-code: fcd58bf66454f924b936f01988e08030 • 📆 2026-06-26

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CPU: AVX2/AVX-512 instruction set required for llama.cpp
RAM: fast 5600MHz+ required to avoid memory bottlenecks
Disk Space:70 GB free space for full FP16 weights storage
Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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.

Specification	Value
Model size	210 MB
Supported languages	100
Input resolution	2048 × 3072 px
Processing speed	> 30 fps

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Seller: Abdul Md

Posted: June 29, 2026

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