MinerU2.5-2509

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Introduction

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

• 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]"

🔗 Ecosystem & Integrations

This model is used in production via the MinerU Open API — no GPU required. Two deployment tracks:

→ MinerU-Ecosystem on GitHub.

🖥️ Self-Hosted — Direct Model Inference

Use mineru-vl-utils to run MinerU2.5 locally on your own GPU.

transformers

pip install "mineru-vl-utils[transformers]"
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
from mineru_vl_utils import MinerUClient

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "opendatalab/MinerU2.5-2509-1.2B", dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(
    "opendatalab/MinerU2.5-2509-1.2B", use_fast=True
)
client = MinerUClient(backend="transformers", model=model, processor=processor)
print(client.two_step_extract(Image.open("/path/to/page.png")))

vllm (recommended — 2.12 fps on A100)

# pip install "mineru-vl-utils[vllm]"
from vllm import LLM
from PIL import Image
from mineru_vl_utils import MinerUClient, MinerULogitsProcessor

client = MinerUClient(
    backend="vllm-engine",
    vllm_llm=LLM(model="opendatalab/MinerU2.5-2509-1.2B",
                 logits_processors=[MinerULogitsProcessor])
)
print(client.two_step_extract(Image.open("/path/to/page.png")))

vllm-async (concurrent batch)

# pip install "mineru-vl-utils[vllm]"
import asyncio, io, 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, MinerULogitsProcessor

async_llm = AsyncLLM.from_engine_args(
    AsyncEngineArgs(model="opendatalab/MinerU2.5-2509-1.2B",
                    logits_processors=[MinerULogitsProcessor])
)
client = MinerUClient(backend="vllm-async-engine", vllm_async_llm=async_llm)

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

asyncio.run(main())
async_llm.shutdown()

☁️ Cloud API — No GPU Required

Free Flash mode available without a token (20 pages / 10 MB per file).

# Windows (PowerShell)
irm https://cdn-mineru.openxlab.org.cn/open-api-cli/install.ps1 | iex

# macOS / Linux
curl -fsSL https://cdn-mineru.openxlab.org.cn/open-api-cli/install.sh | sh

# Flash extract — no login, Markdown only
mineru-open-api flash-extract report.pdf

# Precision extract — token required
mineru-open-api auth
mineru-open-api extract report.pdf -o ./output/

Python SDK

# pip install mineru-open-sdk
from mineru import MinerU

# Flash mode — free, no token
result = MinerU().flash_extract("report.pdf")
print(result.markdown)

# Precision mode — tables, formulas, large files
client = MinerU("your-token")  # https://mineru.net/apiManage/token
result = client.extract("report.pdf")
print(result.markdown)

RAG — LangChain

# pip install langchain-mineru
from langchain_mineru import MinerULoader

# Flash mode — free, no token
docs = MinerULoader(source="report.pdf").load()
print(docs[0].page_content)

# Precision mode — full RAG pipeline
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddin