MinerU2.5-2509-1.2B-GGUF

<span style="color: #7FFF7F;">MinerU2.5-2509-1.2B GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit ee09828cb.

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<!--Begin Original Model Card--> <div align="center"> <p align="center"> <img src="https://raw.githubusercontent.com/opendatalab/MinerU/master/docs/images/MinerU-logo.png" width="300"/> <p> <h1 align="center" style="font-size: 28px"> MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing </h1>

<div align="center"> <a href="https://mineru.net/OpenSourceTools/Extractor" target="_blank" rel="noopener noreferrer"><strong>🚀 Official Demo</strong></a> | <a href="https://arxiv.org/abs/2509.22186" target="_blank" rel="noopener noreferrer"><strong>📄 Technical Report</strong></a> </div> </div>

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<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/performance.jpeg"/> <p>

Introduction

<!-- We present **MinerU2.5**, a 1.2B-parameter VLM-based document parsing model that delivers state-of-the-art accuracy with high efficiency. It adopts a coarse-to-fine, two-stage parsing strategy. A large-scale, diverse data engine supports both pretraining and fine-tuning, enabling robust performance across document types. -->

MinerU2.5 is a 1.2B-parameter vision-language model for document parsing that achieves state-of-the-art accuracy with high computational efficiency. It adopts a two-stage parsing strategy: first conducting efficient global layout analysis on downsampled images, then performing fine-grained content recognition on native-resolution crops for text, formulas, and tables. Supported by a large-scale, diverse data engine for pretraining and fine-tuning, MinerU2.5 consistently outperforms both general-purpose and domain-specific models across multiple benchmarks while maintaining low computational overhead.

Key Improvements

<!-- - **More Precise Layout Detection:** Faithfully preserves non-body elements such as headers, footers, and page numbers, ensuring comprehensive content integrity. - **Significantly Improved Body Text Recognition:** Produces more standardized text formatting with clearly discernible structures for lists, references, and other elements. - **Breakthroughs in Formula Parsing:** Delivers high-quality parsing of complex, lengthy mathematical formulae and accurately recognizes mixed-language (Chinese-English) equations. - **Enhanced Robustness in Table Parsing:** Effortlessly handles challenging cases, including rotated tables, borderless tables, and tables with partial borders. -->

• Comprehensive and Granular Layout Analysis: It not only preserves non-body elements like headers, footers, and page numbers to ensure full content integrity, but also employs a refined and standardized labeling schema. This enables a clearer, more structured representation of elements such as lists, references, and code blocks.
• Breakthroughs in Formula Parsing: Delivers high-quality parsing of complex, lengthy mathematical formulae and accurately recognizes mixed-language (Chinese-English) equations.
• Enhanced Robustness in Table Parsing: Effortlessly handles challenging cases, including rotated tables, borderless tables, and tables with partial borders.

Quick Start

For convenience, we provide mineru-vl-utils, a Python package that simplifies the process of sending requests and handling responses from MinerU2.5 Vision-Language Model. Here we give some examples to use MinerU2.5. For more information and usages, please refer to mineru-vl-utils.

📌 We strongly recommend using vllm for inference, as the vllm-async-engine can achieve a concurrent inference speed of 2.12 fps on one A100.

Install packages

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

transformers Example

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/MinerU2.5-2509-1.2B",
    dtype="auto", # use `torch_dtype` instead of `dtype` for transformers<4.56.0
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "opendatalab/MinerU2.5-2509-1.2B",
    use_fast=True
)

client = MinerUClient(
    backend="transformers",
    model=model,
    processor=processor
)

image = Image.open("/path/to/the/test/image.png")
extracted_blocks = client.two_step_extract(image)
print(extracted_blocks)

vllm-engine Example (Recommended!)

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/MinerU2.5-2509-1.2B",
    logits_processors=[MinerULogitsProcessor]  # if vllm>=0.10.1
)

client = MinerUClient(
    backend="vllm-engine",
    vllm_llm=llm
)

image = Image.open("/path/to/the/test/image.png")
extracted_blocks = client.two_step_extract(image)
print(extracted_blocks)

vllm-async-engine Example (Recommended!)

import io
import asyncio
import aiofiles

from vllm.v1.engine.async_llm import AsyncLLM
from vllm.engine.arg_utils import AsyncEngineArgs
from PIL import Image
from mineru_vl_utils import MinerUClient
from mineru_vl_utils import MinerULogitsProcessor  # if vllm>=0.10.1

async_llm = AsyncLLM.from_engine_args(
    AsyncEngineArgs(
        model="opendatalab/MinerU2.5-2509-1.2B",
        logits_processors=[MinerULogitsProcessor]  # if vllm>=0.10.1
    )
)

client = MinerUClient(
  backend="vllm-async-engine",
  vllm_async_llm=async_llm,
)

async def main():
    image_path = "/path/to/the/test/image.png"
    async with aiofiles.open(image_path, "rb") as f:
        image_data = await f.read()
    image = Image.open(io.BytesIO(image_data))
    extracted_blocks = await client.aio_two_step_extract(image)
    print(extracted_blocks)

asyncio.run(main())

async_llm.shutdown()

Model Architecture

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/Mineru25_framework.jpeg"/> <p>

Performance on OmniDocBench

Across Different Elements

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/omnidocbench_element.jpeg"/> <p>

Across Various Document Types

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/omnidocbench_type.jpeg"/> <p>

Case Demonstration

Full-Document Parsing across Various Doc-Types

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/PDF-Type-1_page_1.png"/> <p> <p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/PDF-Type-2_page_1.png"/> <p> <p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5//PDF-Type-3_page_1.png"/> <p>

Table Recognition

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/Table-Module-1_page_1.png"/> <p> <p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/Table-Module-2_page_1.png"/> <p>

Formula Recognition

<p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/Formula-Module-1_page_1.png"/> <p> <p align="center"> <img alt="Image" src="https://hotelll.github.io/MinerU2.5/Formula-Module-2_page_1.png"/> <p>

Acknowledgements

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

Citation

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

@misc{niu2025mineru25decoupledvisionlanguagemodel,
      title={MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing}, 
      author={Junbo Niu and Zheng Liu and Zhuangcheng Gu and Bin Wang and Linke Ouyang and Zhiyuan Zhao and Tao Chu and Tianyao He and Fan Wu and Qintong Zhang and Zhenjiang Jin and others},
      year={2025},
      eprint={2509.22186},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.22186}, 
}

<!--End Original Model Card-->

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<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
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🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
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Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
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1. "Give me info on my websites SSL certificate"
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Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊