PaddlePaddle/PaddleOCR-VL-1.5-GGUF · Hugging Face (original) (raw)

Introduction

PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency.

Model Architecture

PaddleOCR-VL-1.5 Usage with llama.cpp

Install Dependencies

Install PaddlePaddle and PaddleOCR:

# The following command installs the PaddlePaddle version for CUDA 12.6. For other CUDA versions and the CPU version, please refer to https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html
python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
python -m pip install -U "paddleocr[doc-parser]>=3.4.0"

Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors. For macOS users, please use Docker to set up the environment.

Basic Usage

  1. Start the VLM inference server:
llama-server \  
    -m /path/to/PaddleOCR-VL-1.5-GGUF.gguf \  
    --mmproj /path/to/PaddleOCR-VL-1.5-GGUF-mmproj.gguf  \  
    --port 8080  \  
    --host 0.0.0.0 \  
    --temp 0
  1. Call the PaddleOCR CLI or Python API:
paddleocr doc_parser \  
    -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \  
    --pipeline_version v1.5 \  
    --vl_rec_backend llama-cpp-server \  
    --vl_rec_server_url http://127.0.0.1:8080/v1  
from paddleocr import PaddleOCRVL  
pipeline = PaddleOCRVL(pipeline_version="v1.5", vl_rec_backend="llama-cpp-server", vl_rec_server_url="http://127.0.0.1:8080/v1")  
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")  
for res in output:  
    res.print()  
    res.save_to_json(save_path="output")  
    res.save_to_markdown(save_path="output")

For more usage details and parameter explanations, see the documentation.

PaddleOCR-VL-1.5-0.9B Usage with llama.cpp

Currently, the PaddleOCR-VL-1.5-0.9B model facilitates seamless inference via the transformers library, supporting comprehensive text spotting and the recognition of complex elements including formulas, tables, charts, and seals. Below is a simple script we provide to support inference using the PaddleOCR-VL-1.5-0.9B model with llama.cpp.

Usage Tips

We have six types of element-level recognition:

git clone https://github.com/ggml-org/llama.cpp.git  
cd llama.cpp  
python -m pip install gguf  
python ./gguf-py/gguf/scripts/gguf_set_metadata.py /path/to/PaddleOCR-VL-1.5-GGUF/PaddleOCR-VL-1.5-mmproj.gguf clip.vision.image_max_pixels 1605632 --force  
# back to default value (1003520):  
# python ./gguf-py/gguf/scripts/gguf_set_metadata.py /path/to/PaddleOCR-VL-1.5-GGUF/PaddleOCR-VL-1.5-mmproj.gguf clip.vision.image_max_pixels 1003520 --force

llama-cli Usage

llama-cli \
    -m /path/to/PaddleOCR-VL-1.5-GGUF/PaddleOCR-VL-1.5.gguf \
    --mmproj /path/to/PaddleOCR-VL-1.5-GGUF/PaddleOCR-VL-1.5-mmproj.gguf \
    -p 'OCR:' \
    --image 'test_image.jpg'

llama-server Usage

llama-server -m /path/to/PaddleOCR-VL-1.5.gguf --mmproj /path/to/PaddleOCR-VL-1.5-GGUF/PaddleOCR-VL-1.5-mmproj.gguf --temp 0

Acknowledgements

We extend our gratitude to @megemini for their significant pull request, which adds support for PaddleOCR-VL-1.5-0.9B and PaddleOCR-VL-0.9B models to llama.cpp.