Image-Text-to-Text
MLX
Safetensors
PaddleOCR
English
Chinese
multilingual
paddleocr_vl
ERNIE4.5
PaddlePaddle
image-to-text
ocr
conversational
custom_code
4-bit precision
Instructions to use translate-studio/PaddleOCR-VL-1.5-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use translate-studio/PaddleOCR-VL-1.5-4bit 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("translate-studio/PaddleOCR-VL-1.5-4bit") config = load_config("translate-studio/PaddleOCR-VL-1.5-4bit") # 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) - PaddleOCR
How to use translate-studio/PaddleOCR-VL-1.5-4bit with PaddleOCR:
# Please refer to the document for information on how to use the model. # https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/module_overview.html
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- b81bcd686de434961e297c5797a82ccd6eac2b980de03ea39aaa0623b80f8cbd
- Size of remote file:
- 11.2 MB
- SHA256:
- c8a215a59183d0d0781adc33bacd3ce6162716f7fd568fb30234a74d69803a7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.