1.1. Install packages

bash
Copy code
# For `transformers` backend
pip install "mineru-vl-utils[transformers]"
# For `vllm-engine` and `vllm-async-engine` backend
pip install "mineru-vl-utils[vllm]"

1.2. transformers Example

python
Copy code
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
from mineru_vl_utils import MinerUClient

# for transformers>=4.56.0
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "opendatalab/MinerU2.5-Pro-2604-1.2B", dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "opendatalab/MinerU2.5-Pro-2604-1.2B", use_fast=True
)

client = MinerUClient(
    backend="transformers", model=model, processor=processor,
    image_analysis=False # default False, set True to enable image/chart analysis
)

print(client.two_step_extract(Image.open("/path/to/page.png")))

1.3. vllm-engine Example (Recommended!)

python
Copy code
from vllm import LLM
from PIL import Image
from mineru_vl_utils import MinerUClient
from mineru_vl_utils import MinerULogitsProcessor  # if vllm>=0.10.1

llm = LLM(
    model="opendatalab/MinerU2.5-Pro-2604-1.2B",
    logits_processors=[MinerULogitsProcessor]  # if vllm>=0.10.1
)

client = MinerUClient(
    backend="vllm-engine", vllm_llm=llm,
    image_analysis=False # default False, set True to enable image/chart analysis
)

print(client.two_step_extract(Image.open("/path/to/page.png")))

1.4. JSON result to Markdown (enable truncated paragraph merging)

python
Copy code
from mineru_vl_utils.post_process import json2md

# ... omit client initialize
content_list = client.two_step_extract(Image.open("path/to/page.png"))
md_res = json2md(content_list)

🚧 Cross-Page Table Merging: Currently under integration. Stay tuned!

2. Performance

2.1. End-to-End Document Parsing on OmniDocBench v1.6

2.2. Text Recognition

2.3. Formula Recognition

2.4. Table Recognition

3. Showcase

3.1. Basic Parsing Capability

3.2. Extra Supported Features

4. Acknowledgement & Citation

We would like to thank Qwen Team, vLLM, OmniDocBench, PaddleOCR, UniMERNet, DocLayout-YOLO for providing valuable code and models. We also appreciate everyone's contribution to this open-source project!

If you find our work useful in your research, please consider giving a star ⭐ and citation 📝 :

BibTeX
Copy code
@misc{wang2026mineru25propushinglimitsdatacentric,
      title={MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale}, 
      author={Bin, Wang and Tianyao, He and Linke, Ouyang and Fan, Wu and Zhiyuan, Zhao and Tao, Chu and Yuan, Qu and Zhenjiang, Jin and Weijun, Zeng and Ziyang, Miao and Bangrui, Xu and Junbo, Niu and others},
      year={2026},
      eprint={2604.04771},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.04771}, 
}