Image-Text-to-Text
MLX
Safetensors
multilingual
deepseekocr
mlx-vlm
ocr
vision-language
baidu
conversational
Instructions to use mikoy92/Unlimited-OCR-bf16-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mikoy92/Unlimited-OCR-bf16-mlx 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("mikoy92/Unlimited-OCR-bf16-mlx") config = load_config("mikoy92/Unlimited-OCR-bf16-mlx") # 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 BF16 MLX
MLX conversion of baidu/Unlimited-OCR.
from mlx_vlm import load
from mlx_vlm.generate import generate
model, processor = load("mikoy92/Unlimited-OCR-bf16-mlx")
result = generate(
model,
processor,
"<image>\ndocument parsing.",
image="your_image.jpg",
max_tokens=512,
temperature=0.0,
)
print(result.text)
CLI:
python -m mlx_vlm generate \
--model mikoy92/Unlimited-OCR-bf16-mlx \
--image your_image.jpg \
--prompt "document parsing." \
--temp 0
This repo stores MLX-layout weights in safetensors (format=mlx). It uses the existing deepseekocr MLX implementation because Unlimited-OCR shares that SAM + CLIP-L + DeepSeekV2 OCR architecture shape with different checkpoint dimensions.
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Model size
3B params
Tensor type
BF16
·
Hardware compatibility
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