AI Briefingdots.ocr, Core ML Optimization for Apple Devices
Key point
Introduces methods for converting RedNote's 3B-parameter OCR model, dots.ocr, to Core ML and MLX to run on the Apple Neural Engine.
Details
RedNote's dots.ocr (3B parameters) is an OCR model that recorded performance surpassing Gemini 2.5 Pro on OmniDocBench. Running this model on Apple's Neural Engine achieves 12x higher power efficiency compared to CPU and 4x higher compared to GPU.
This guide covers the technical process of optimizing a PyTorch model for on-device environments using a combination of Core ML and MLX.
Key Model Architecture and Conversion Strategy:
- Model Composition: Consists of a NaViT-based 1.2B vision encoder and a Qwen2.5-1.5B backbone.
- Conversion Process: Capture the execution graph with
torch.jit.traceortorch.export, then compile to.mlpackageviacoremltools. - Hybrid Execution: The vision encoder is optimized to leverage the Neural Engine through Core ML, while the LM backbone is optimized to leverage the GPU through MLX.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.
Related
Underdog Releases 'Husky', a Model-Specific Inference Engine Up to 4.5x Faster than MLX