infinity-parser2 0.2.0

Document parsing Python package supporting PDF and image parsing using Infinity-Parser2-Pro model.

Infinity-Parser2

🤗 Model | 📊 Dataset (coming soon...) | 📄 Paper (coming soon...) | 🚀 Demo (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

API Reference

InfinityParser2

parser = InfinityParser2(model_name="infly/Infinity-Parser2-Pro")

Parameter	Type	Default	Description
model_name	str	"infly/Infinity-Parser2-Pro"	HuggingFace model name or local path
backend	str	"vllm-engine"	Inference backend: "transformers", "vllm-engine", or "vllm-server"
tensor_parallel_size	int	None	GPU count by default. Tensor parallel size for vLLM Engine
device	str	"cuda"	Only "cuda" is supported
api_url	str	"http://localhost:8000/v1/chat/completions"	API URL for vLLM Server backend
api_key	str	"EMPTY"	API key for vLLM Server backend
min_pixels	int	2048	Minimum pixel count for image input (transformers backend only)
max_pixels	int	16777216	Maximum pixel count (~4096x4096), transformers backend only
model_cache_dir	str	None	Model cache directory (defaults to ~/.cache/infinity_parser2/)

parse()

result = parser.parse("demo_data/demo.pdf")

Parameter	Type	Default	Description
input_data	str | List[str] | PIL.Image	Required	File path(s), directory path, or PIL Image object
task_type	str	"doc2json"	"doc2json" (layout to JSON) | "doc2md" (direct Markdown) | "custom"
custom_prompt	str	None	Custom prompt; required when task_type="custom"
batch_size	int	4	Number of images to process per batch
output_dir	str	None	If set, saves results to this directory instead of returning them
output_format	str	"md"	"md" | "json". Only "md" is supported for doc2md / custom tasks
**kwargs	—	—	Additional args passed to the model (e.g., max_new_tokens, temperature)

Return Values

Input	output_dir=None	output_dir set
Single file	str	None
List of files	List[str]	None
Directory	Dict[str, str] (path→content)	None

When output_dir is set, results are saved to output_dir/{filename}/result.md (or result.json).

Advanced Usage

Model Caching

Models are downloaded automatically on first use and cached at ~/.cache/infinity_parser2/. You can customize the cache location:

parser = InfinityParser2(
    model_name="infly/Infinity-Parser2-Pro",
    model_cache_dir="/path/to/cache"
)

Generation Parameters

result = parser.parse(
    "demo_data/demo.pdf",
    max_new_tokens=16384,
    temperature=0.01,
    top_p=0.95,
)

Utility Functions

from infinity_parser2 import (
    convert_pdf_to_images,
    convert_json_to_markdown,
    extract_json_content,
    get_files_from_directory,
    is_supported_file,
    SUPPORTED_TASK_TYPES,
    ModelCache,
    get_model_cache,
)

# Convert PDF pages to PIL Images
images = convert_pdf_to_images("demo_data/demo.pdf", dpi=300)

# Convert layout JSON to Markdown
markdown = convert_json_to_markdown(json_string)

# Check model cache
cache = get_model_cache()
print(cache.resolve_model_path("infly/Infinity-Parser2-Pro"))

Requirements

• Python 3.12+
• CUDA-compatible GPU
• See setup.py for full dependency list.

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.