Image-Text-to-Text
MLX
Safetensors
multilingual
deepseekocr
mlx-vlm
quantized
apple-silicon
deepseek-ocr
ocr
vision-language
multimodal
document-parsing
conversational
8-bit precision
Instructions to use sahilchachra/unlimited-ocr-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sahilchachra/unlimited-ocr-8bit-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("sahilchachra/unlimited-ocr-8bit-mlx") config = load_config("sahilchachra/unlimited-ocr-8bit-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
- Xet hash:
- daa5fba58ad1efa25dcb339f6f98193fb422b8c1f483d5f6d6ce786c4b32e490
- Size of remote file:
- 3.92 GB
- SHA256:
- 3f810f3f3e3a896299080b3c6e4c799fc9be2d9e979f2fb0151f9bc090d49f82
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