Image-Text-to-Text
MLX
Core ML
apple
ocr
document-parsing
multi-page
deepseek-ocr
Mixture of Experts
fluidinference
Instructions to use FluidInference/unlimited-ocr-mlx-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FluidInference/unlimited-ocr-mlx-coreml with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("FluidInference/unlimited-ocr-mlx-coreml") config = load_config("FluidInference/unlimited-ocr-mlx-coreml") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
3 days ago
-
Card: turbo decoder
Verified signature4568571 -
MLX turbo decoder: routed experts 4-bit (group 32), rest 8-bit; ~10-17% faster, text within ~1 pt of 8-bit
Verified signature9d5f274 -
Card: MLX + Core ML (repo renamed from unlimited-ocr-coreml)
Verified signatured75f5d8 -
MLX text decoder (8-bit affine g64, decoder only; page encoder stays Core ML): ~5x faster decode
Verified signaturec32f06c
4 days ago
-
Card: decode speed after the attention change
Verified signature2ab6895 -
Decoder: concatenate attention scores instead of K/V (no per-layer cache copy, ~17% faster decode, same output)
Verified signature51046f0
5 days ago
-
Unlimited-OCR Core ML: fp16 page encoder + int8 stateful multi-page decoder
Verified signature9921f1f -
initial commit
Verified signature90fa493