Instructions to use mlx-community/Unlimited-OCR-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Unlimited-OCR-8bit 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("mlx-community/Unlimited-OCR-8bit") config = load_config("mlx-community/Unlimited-OCR-8bit") # 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
Unlimited-OCR — MLX 8bit
This repository contains the 8-bit affine quantized MLX conversion of Baidu's official baidu/Unlimited-OCR checkpoint. It is published for Apple Silicon inference with mlx-vlm and maintained for use by shuuul/aimd.
The model architecture, tokenizer, processor configuration, prompts, and MIT license come from the official checkpoint. Refer to the official model card and Unlimited-OCR repository for model details, capabilities, limitations, and attribution.
Conversion details
| Setting | Value |
|---|---|
| Source | baidu/Unlimited-OCR |
| Tool | mlx-vlm 0.6.8 |
| MLX runtime used for conversion | mlx 0.32.0 |
| Output precision | 8bit |
| Quantization | affine, group size 64 |
| Repository size | 3.66 GiB |
The language-model weights use uniform affine quantization with group size 64. mlx-vlm intentionally leaves unsupported or quality-sensitive multimodal modules at higher precision, so total repository size is larger than a theoretical all-layer quantization.
Conversion command:
mlx_vlm.convert \
--hf-path baidu/Unlimited-OCR \
--mlx-path ./Unlimited-OCR-8bit \
--quantize --q-bits 8 --q-group-size 64 --q-mode affine
Use with AIMD
On macOS/Apple Silicon, install AIMD and run OCR directly:
uv tool install --force "aimd-tool @ git+https://github.com/shuuul/aimd.git@main"
aimd scan.pdf --task ocr --model unlimited_ocr_8bit
unlimited_ocr defaults to the 4-bit repository. AIMD runs each page in Unlimited-OCR's single-image gundam mode with prompt document parsing., cropping=True, base_size=1024, image_size=640, and a sliding-window no-repeat n-gram guard. See the AIMD README for the complete runtime behavior.
Use with mlx-vlm
pip install -U "mlx-vlm>=0.6.8"
python -m mlx_vlm.generate \
--model mlx-community/Unlimited-OCR-8bit \
--image scan.png \
--prompt "document parsing." \
--max-tokens 8192 \
--temperature 0
Unlimited-OCR support first shipped in mlx-vlm 0.6.4; 0.6.8 or newer is recommended to match the conversion environment.
Available MLX weights
| Precision | Repository | Size |
|---|---|---|
| 4bit | mlx-community/Unlimited-OCR-4bit |
2.29 GiB |
| 6bit | mlx-community/Unlimited-OCR-6bit |
2.98 GiB |
| 8bit | mlx-community/Unlimited-OCR-8bit |
3.66 GiB |
| bf16 | mlx-community/Unlimited-OCR-bf16 |
6.22 GiB |
License and attribution
The upstream checkpoint is released under the MIT license. This repository is a format conversion and does not modify the upstream model architecture or claim ownership of Baidu's model.
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Model tree for mlx-community/Unlimited-OCR-8bit
Base model
baidu/Unlimited-OCR