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
4-bit precision
Instructions to use mlx-community/PaddleOCR-VL-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use mlx-community/PaddleOCR-VL-4bit 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-4bit") 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-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("mlx-community/PaddleOCR-VL-4bit") config = load_config("mlx-community/PaddleOCR-VL-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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- ERNIE4.5
- PaddleOCR
- PaddlePaddle
- image-to-text
- ocr
- document-parse
- layout
- table
- formula
- chart
- mlx
base_model: baidu/ERNIE-4.5-0.3B-Paddle
language:
- en
- zh
- multilingual
library_name: PaddleOCR
mlx-community/PaddleOCR-VL-4bit
This model was converted to MLX format from PaddlePaddle/PaddleOCR-VL using mlx-vlm version 0.3.10.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/PaddleOCR-VL-4bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>