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
Instructions to use mlx-community/PaddleOCR-VL-bfloat16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use mlx-community/PaddleOCR-VL-bfloat16 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-bfloat16") 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-bfloat16 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-bfloat16") config = load_config("mlx-community/PaddleOCR-VL-bfloat16") # 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:
- 91d3103d1c34e38bd037ae91308bef113b436b5be7ae4e84d56dead8c96b6abd
- Size of remote file:
- 1.81 GB
- SHA256:
- 7f958725629c2f9ee3fec3665be6371930893ca42fe92477c8f527ba431ad1e4
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