Image-Text-to-Text
MLX
Safetensors
unlimited-ocr
ax-engine
mlx-vlm
ocr
mxfp8
int8
apple-silicon
automatosx
conversational
8-bit precision
Instructions to use AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8 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("AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8") config = load_config("AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8") # 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
Fix model card tags: ax-engine, automatosx (YAML normalize)
Browse files
README.md
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---
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license: mit
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base_model: baidu/Unlimited-OCR
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library_name: mlx
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pipeline_tag: image-text-to-text
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tags:
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- ax-engine
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- mlx
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- mlx-vlm
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- ocr
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- mxfp8
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- int8
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- apple-silicon
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- unlimited-ocr
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- automatosx
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widget:
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- text: "<image>document parsing."
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---
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+
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license: mit
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base_model: baidu/Unlimited-OCR
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library_name: mlx
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pipeline_tag: image-text-to-text
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tags:
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- ax-engine
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- mlx
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- mlx-vlm
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- ocr
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- mxfp8
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- int8
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- apple-silicon
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- unlimited-ocr
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- automatosx
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widget:
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- text: "<image>document parsing."
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