README.md

# Infinity-Parser2-Pro

<p align="center">
    <img src="assets/logo.png" width="400"/>
<p>

<p align="center">
💻 <a href="https://github.com/infly-ai/INF-MLLM">Github</a> |
📊 <a>Dataset (coming soon...)</a> |
📄 <a>Paper (coming soon...)</a> |
🚀 <a>Demo (coming soon...)</a>
</p>

## 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

<p align="left">
    <img src="assets/document_parsing_performance_evaluation.png" width="1200"/>
<p>

## Quick Start

### 1. Minimal "Hello World" (Native Transformers)

If you are looking for a minimal script to parse a single image to Markdown using the native `transformers` library, here is a simple snippet:

```python
from PIL import Image
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from qwen_vl_utils import process_vision_info

# Load the model and processor
model = AutoModelForImageTextToText.from_pretrained(
    "infly/Infinity-Parser2-Pro",
    torch_dtype="float16",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("infly/Infinity-Parser2-Pro")

# Build the messages for the model
pil_image = Image.open("demo_data/demo.png").convert("RGB")
min_pixels = 2048  # 32 * 64
max_pixels = 16777216  # 4096 * 4096
prompt = """
Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox.
1. Bbox format: [x1, y1, x2, y2]
2. Layout Categories: The possible categories are ['header', 'title', 'text', 'figure', 'table', 'formula', 'figure_caption', 'table_caption', 'formula_caption', 'figure_footnote', 'table_footnote', 'page_footnote', 'footer'].
3. Text Extraction & Formatting Rules:
    - Figure: For the 'figure' category, the text field should be empty string.
    - Formula: Format its text as LaTeX.
    - Table: Format its text as HTML.
    - All Others (Text, Title, etc.): Format their text as Markdown.
4. Constraints:
    - The output text must be the original text from the image, with no translation.
    - All layout elements must be sorted according to human reading order.
5. Final Output: The entire output must be a single JSON object.
"""

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": pil_image,
                "min_pixels": min_pixels,
                "max_pixels": max_pixels,
            },
            {"type": "text", "text": prompt},
        ],
    }
]

chat_template_kwargs = {"enable_thinking": False}

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, **chat_template_kwargs
)
image_inputs, _ = process_vision_info(messages, image_patch_size=16)

inputs = processor(
    text=text,
    images=image_inputs,
    do_resize=False,
    padding=True,
    return_tensors="pt",
)

# Move all tensors to the same device as the model
inputs = {
    k: v.to(model.device) if isinstance(v, torch.Tensor) else v
    for k, v in inputs.items()
}

# Generate the response
generated_ids = model.generate(
    **inputs,
    max_new_tokens=32768,
    temperature=0.0,
    top_p=1.0,
)

# Strip input tokens, keeping only the newly generated response
generated_ids_trimmed = [
    out_ids[len(in_ids) :]
    for in_ids, out_ids in zip(inputs["input_ids"], generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

### 2. Advanced Pipeline (infinity_parser2)

For bulk processing, advanced features, or an end-to-end PDF parsing pipeline, we recommend using our infinity_parser2 wrapper.

#### Pre-requisites

```bash
# 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 (FlashAttention-2 is recommended by default)
# Standard install (compiles from source, ~10-30 min):
pip install flash-attn==2.8.3 --no-build-isolation
# Faster install: download wheel from https://github.com/Dao-AILab/flash-attention/releases. Then run: pip install /path/to/<wheel_filename>.whl
# For Hopper GPUs (e.g. H100, H800), we recommend FlashAttention-3 instead. See: https://github.com/Dao-AILab/flash-attention
# NOTE: The code will prioritize detecting FlashAttention-3. If not found, it falls back to FlashAttention-2.

# 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

Install from PyPI

```bash
pip install infinity_parser2
```

Install from source code

```bash
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.

```bash
# 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
```

```bash
# View all options
parser --help
```

##### Python API

```python
# 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 |

```python
# 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).

```python
# 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:

```bash
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](https://github.com/infly-ai/INF-MLLM/blob/main/Infinity-Parser2).

## Acknowledgments

We would like to thank [Qwen3.5](https://github.com/QwenLM/Qwen3.5), [ms-swift](https://github.com/modelscope/ms-swift), [VeRL](https://github.com/verl-project/verl), [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval), [olmocr](https://huggingface.co/datasets/allenai/olmOCR-bench), [PaddleOCR-VL](https://github.com/PaddlePaddle/PaddleOCR), [MinerU](https://github.com/opendatalab/MinerU), [dots.ocr](https://github.com/rednote-hilab/dots.ocr), [Chandra-OCR-2](https://github.com/datalab-to/chandra) for providing dataset, code and models.

## License

This model is licensed under apache-2.0.