Image-Text-to-Text
MLX
Safetensors
paddleocr_vl
ocr
document-parsing
multimodal
vision-language
conversational
custom_code
5-bit
Instructions to use mlx-community/PaddleOCR-VL-1.6-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/PaddleOCR-VL-1.6-5bit 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/PaddleOCR-VL-1.6-5bit") config = load_config("mlx-community/PaddleOCR-VL-1.6-5bit") # 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
- Xet hash:
- ae4b922e41b4f2c8b97304715a840a47d968483d9f8b1b16c25d7d440dbee687
- Size of remote file:
- 11.2 MB
- SHA256:
- f64dee5b500fd0737498420a95cfa25927990ecbe37c0c112a6c8fcea22bc7f9
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