Run chandra-ocr-2
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Run chandra-ocr-2

Run chandra-ocr-2

To install this model locally in the shortest time, opt for a direct curl execution.

Kindly follow the on-screen instructions below.

The system automatically triggers a cloud download for all heavy weights.

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

🔧 Digest: 273522206a9c886673cdc4d6f1c9a80a • 🕒 Updated: 2026-06-28
  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: free: 80 GB on system drive for scratch space
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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Setting up this model locally is incredibly fast if you use the native CMD prompt.

Refer to the action plan below to initialize the model.

The download manager will automatically pull several gigabytes of data.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🔗 SHA sum: ab2aa2c46dc1cdc5f2c4d84d097fa1ac | Updated: 2026-06-28
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

MiniMax-M2.7-NVFP4 is a highly optimized, 4-bit quantized variant of MiniMaxAI’s flagship 230-billion parameter sparse Mixture-of-Experts (MoE) foundation model, compressed via NVIDIA Model Optimizer using the cutting-edge NVFP4 (Nvidia Floating Point 4-bit) format. The architecture leverages a blockwise FP8 scaling scheme per 16 elements, dropping the previous Lightning Attention layers in favor of pure, hardware-optimized Grouped-Query Attention (GQA) with 48 query heads and 8 KV heads. This aggressive mathematical alignment allows the massive model to execute on a mere 10B active parameters per token, reducing VRAM demands dramatically down to 70 GB per GPU in Tensor Parallel setups. Tailored for self-evolving agent loops, multi-file code refactoring, and real-world system debugging, it delivers extreme processing throughput over an expansive 196,608-token context window while maintaining an exceptional 56.22% score on the SWE-Pro engineering benchmark.

Specification Detail
Total / Active Parameters 230 Billion Total / 10 Billion Active per Token (Sparse MoE)
Quantization Layout NVFP4 (4-bit Weights with Blockwise FP8 Scales via Nvidia Model Optimizer)
Context Window 196,608 tokens (196k natively)
Hardware Baseline Dual NVIDIA RTX PRO 6000 Blackwell (96GB GDDR7) or H100 Tensor Parallel
Attention Mechanism Standard GQA Softmax (48 Query / 8 KV Heads)
Primary Execution Engines vLLM Native Server, SGLang Backend with b12x
Core Benchmarks SWE-Pro: 56.22% / Terminal Bench 2: 57.0% / VIBE-Pro: 55.6%
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