Image-Text-to-Text
PaddleOCR
Safetensors
MLX
English
Chinese
multilingual
paddleocr_vl
ERNIE4.5
PaddlePaddle
image-to-text
ocr
document-parse
layout
table
formula
chart
conversational
custom_code
5-bit
Instructions to use mlx-community/PaddleOCR-VL-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use mlx-community/PaddleOCR-VL-5bit with PaddleOCR:
# See https://www.paddleocr.ai/latest/version3.x/pipeline_usage/PaddleOCR-VL.html to installation from paddleocr import PaddleOCRVL pipeline = PaddleOCRVL(pipeline_version="mlx-community/PaddleOCR-VL-5bit") output = pipeline.predict("path/to/document_image.png") for res in output: res.print() res.save_to_json(save_path="output") res.save_to_markdown(save_path="output") - MLX
How to use mlx-community/PaddleOCR-VL-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-5bit") config = load_config("mlx-community/PaddleOCR-VL-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
- LM Studio
- Xet hash:
- 1bf1153f10b620f6d43f72a25d6d0f17a1686923967780411dc102bf9601640c
- Size of remote file:
- 11.2 MB
- SHA256:
- 7b15309ef3d06ec9607f1b6da7ef4824cad3e7e492ce8d4cc4f66584b39104d2
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