Infinity-Parser2-Pro

💻 Github | 📊 Dataset (coming soon...) | 📄 Paper (coming soon...) | 🚀 Demo (coming soon...)

News

[2026-04-14] We uploaded the quick start guide for Infinity-Parser2. Feel free to contact us if you have any questions.
[2026-04-11] We released Infinity-Parser2-Pro, our flagship document parsing model — now available as a preview. Stay tuned: the official release, the lightweight Infinity-Parser2-Flash, and our multimodal parsing dataset Infinity-Doc2-10M are coming soon.

Introduction

We are excited to release Infinity-Parser2-Pro, our latest flagship document understanding model that achieves a new state-of-the-art on olmOCR-Bench with a score of 86.7%, surpassing frontier models such as DeepSeek-OCR-2, PaddleOCR-VL, and dots.mocr. Building on our previous model Infinity-Parser-7B, we have significantly enhanced our data engine and multi-task reinforcement learning approach. This enables the model to consolidate robust multi-modal parsing capabilities into a unified architecture, delivering brand-new zero-shot capabilities for diverse real-world business scenarios.

Key Features

Upgraded Data Engine: We have comprehensively enhanced our synthetic data engine to support both fixed-layout and flexible-layout document formats. By generating over 1 million diverse full-text samples covering a wide range of document layouts, combined with a dynamic adaptive sampling strategy, we ensure highly balanced and robust multi-task learning across various document types.
Multi-Task Reinforcement Learning: We designed a novel verifiable reward system to support Joint Reinforcement Learning (RL), enabling seamless and simultaneous co-optimization of multiple complex tasks, including doc2json and doc2markdown.
Breakthrough Parsing Performance: It substantially outperforms our previous 7B model, achieving 86.7% on olmOCR-Bench, surpassing frontier models such as DeepSeek-OCR-2, PaddleOCR-VL, and dots.mocr.
Inference Acceleration: By adopting the highly efficient MoE architecture, our inference throughput has increased by 21% (from 441 to 534 tokens/sec), reducing deployment latency and costs.

Performance

Quick Start

Installation

Pre-requisites

# Create a Conda environment (Optional)
conda create -n infinity_parser2 python=3.12
conda activate infinity_parser2

# Install PyTorch (CUDA). Find the proper version at https://pytorch.org/get-started/previous-versions based on your CUDA version.
pip install torch==2.10.0 torchvision==0.25.0 torchaudio==2.10.0 --index-url https://download.pytorch.org/whl/cu128

# Install FlashAttention (required for NVIDIA GPUs).
# This command builds flash-attn from source, which can take 10 to 30 minutes.
pip install flash-attn==2.8.3 --no-build-isolation
# For Hopper GPUs (e.g. H100, H800), we recommend FlashAttention-3 instead. See the official guide at https://github.com/Dao-AILab/flash-attention.

# Install vLLM
# NOTE: you may need to run the command below to resolve triton and numpy conflicts before installing vllm.
# pip uninstall -y pytorch-triton opencv-python opencv-python-headless numpy && rm -rf "$(python -c 'import site; print(site.getsitepackages()[0])')/cv2"
pip install vllm==0.17.1

Install infinity_parser2

# From PyPI
pip install infinity_parser2

# From source
git clone https://github.com/infly-ai/INF-MLLM.git
cd INF-MLLM/Infinity-Parser2
pip install -e .

Usage

Command Line

The parser command is the fastest way to get started.

# NOTE: The Infinity-Parser2 model will be automatically downloaded on the first run.

# Parse a PDF (outputs Markdown by default)
parser demo_data/demo.pdf

# Parse an image
parser demo_data/demo.png

# Batch parse multiple files
parser demo_data/demo.pdf demo_data/demo.png -o ./output

# Parse an entire directory
parser demo_data -o ./output

# Output raw JSON with layout bboxes
parser demo_data/demo.pdf --output-format json

# Convert to Markdown directly
parser demo_data/demo.png --task doc2md

# View all options
parser --help

Python API

# NOTE: The Infinity-Parser2 model will be automatically downloaded on the first run.

from infinity_parser2 import InfinityParser2

parser = InfinityParser2()

# Parse a single file (returns Markdown)
result = parser.parse("demo_data/demo.pdf")
print(result)

# Parse multiple files (returns list)
results = parser.parse(["demo_data/demo.pdf", "demo_data/demo.png"])

# Parse a directory (returns dict)
results = parser.parse("demo_data")

Output formats:

task_type	Description	Default Output
`doc2json`	Extract layout elements with bboxes (default)	Markdown
`doc2md`	Directly convert to Markdown	Markdown
`custom`	Use your own prompt	Raw model output

# doc2json: get raw JSON with bbox coordinates
result = parser.parse("demo_data/demo.pdf", output_format="json")

# doc2md: direct Markdown conversion
result = parser.parse("demo_data/demo.pdf", task_type="doc2md")

# Custom prompt
result = parser.parse("demo_data/demo.pdf", task_type="custom",
                      custom_prompt="Please transform the document's contents into Markdown format.")

# Batch processing with custom batch size
result = parser.parse("demo_data", batch_size=8)

# Save results to directory
parser.parse("demo_data/demo.pdf", output_dir="./output")

Backends:

Infinity-Parser2 supports three inference backends. By default it uses the vLLM Engine (offline batch inference).

# vLLM Engine (default) — offline batch inference
parser = InfinityParser2(
    model_name="infly/Infinity-Parser2-Pro",
    backend="vllm-engine",        # default
    tensor_parallel_size=2,
)

# Transformers — local single-GPU inference
parser = InfinityParser2(
    model_name="infly/Infinity-Parser2-Pro",
    backend="transformers",
    device="cuda",
    torch_dtype="bfloat16",       # "float16" or "bfloat16"
)

# vLLM Server — online HTTP API (start server first)
parser = InfinityParser2(
    model_name="infly/Infinity-Parser2-Pro",
    backend="vllm-server",
    api_url="http://localhost:8000/v1/chat/completions",
    api_key="EMPTY",
)

To start a vLLM server:

vllm serve infly/Infinity-Parser2-Pro \
    --trust-remote-code \
    --reasoning-parser qwen3 \
    --host 0.0.0.0 \
    --port 8000 \
    --tensor-parallel-size 2 \
    --gpu-memory-utilization 0.85 \
    --max-model-len 65536 \
    --mm-encoder-tp-mode data \
    --mm-processor-cache-type shm \
    --enable-prefix-caching

For more details, please refer to the official guide.

Acknowledgments

We would like to thank Qwen3.5, ms-swift, VeRL, lmms-eval, olmocr, PaddleOCR-VL, MinerU, dots.ocr, Chandra-OCR-2 for providing dataset, code and models.

License

This model is licensed under apache-2.0.