Update README.md

README.md

@@ -11,35 +11,187 @@
 🚀 <a>Demo (coming soon...)</a>
 </p>
 
-# News
+## News
 
-- [2026-04-11] We release 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.
+- [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
+## 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
+### 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
+## Performance
 
 <p align="left">
     <img src="assets/document_parsing_performance_evaluation.png" width="1200"/>
 <p>
 
-# Quick Start
+## Quick Start
 
-Coming soon...
+### Installation
 
-# Citation
+#### Pre-requisites
 
-Coming soon...
+```bash
+# Install PyTorch (CUDA). Find the proper version on the [official site](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
 
-# License
+# 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](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
+
+```bash
+# 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.
+
+```bash
+# 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
+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="Extract the title and authors only.")
+
+# 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 model details, please refer to [offical 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.