Instructions to use tencent/Youtu-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Youtu-Parsing with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="tencent/Youtu-Parsing", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tencent/Youtu-Parsing", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/Youtu-Parsing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Youtu-Parsing"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Youtu-Parsing",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Youtu-Parsing

SGLang

How to use tencent/Youtu-Parsing with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tencent/Youtu-Parsing" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Youtu-Parsing",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tencent/Youtu-Parsing" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Youtu-Parsing",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use tencent/Youtu-Parsing with Docker Model Runner:
```
docker model run hf.co/tencent/Youtu-Parsing
```

📃 License • 👨‍💻 Code • 🖥️ Demo • 📑 Technical Report • 📊 Benchmarks • 🚀 Getting Started

🎯 Introduction

Youtu-Parsing is a specialized document parsing model built upon the open-source Youtu-LLM 2B foundation. By extending the capabilities of the base model with a prompt-guided framework and NaViT-style dynamic visual encoder, Youtu-Parsing offers enhanced parsing capabilities for diverse document elements including text, tables, formulas, and charts. The model incorporates an efficient parallel decoding mechanism that significantly accelerates inference, making it practical for real-world document analysis applications. We share Youtu-Parsing with the community to facilitate research and development in document understanding.

✨ Key Features

📄 Document Structure Preservation

Text Localization: Accurately detects and localizes text regions with pixel-level precision, ensuring no content is missed or misplaced across diverse document layouts.
Reading Order Restoration: Intelligently reconstructs the logical reading sequence of document content, maintaining proper flow across columns, sections, and pages for coherent understanding.

📊 Advanced Content Recognition

Text Recognition: Provides precise text recognition across diverse scenarios.
Formula Recognition: Automatically converts mathematical expressions to LaTeX format.
Table Recognition: Automatically detects tables and converts them to HTML format.
Chart Recognition: Converts charts to markdown tables, mind maps and flow charts to mermaid format.

⚡ High-Performance Inference

Token Parallelism: Enables simultaneous inference of multiple tokens for accelerated processing, achieving 5-11x speedup.
Query Parallelism: Combines multiple queries together to maximize Token Parallelism benefits, providing an additional 2x speedup on top of Token Parallelism acceleration.

📊 Performance

1. OminiDocBench v1.5

2. olmOCR

🚀 Quick Start

Install packages

conda create -n youtu_parsing python=3.10
conda activate youtu_parsing
pip install git+https://github.com/TencentCloudADP/youtu-parsing.git#subdirectory=youtu_hf_parser

# install the flash-attn2
# For CUDA 12.x + PyTorch 2.6 + Python 3.10 + Linux x86_64:
pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp310-cp310-linux_x86_64.whl

# Alternative: Install from PyPI
pip install flash-attn==2.7.0

Usage with transformers

from youtu_hf_parser import YoutuOCRParserHF

# Initialize the parser
parser = YoutuOCRParserHF(
    model_path=model_path,
    enable_angle_correct=True,  # Set to False to disable angle correction
    angle_correct_model_path=angle_correct_model_path
)

# Parse an image
parser.parse_file(input_path=image_path, output_dir=output_dir)

🎨 Visualization

Text Recognition

Formula Recognition

Table Recognition

Chart Recognition

🤝 Acknowledgements

We would like to thank Youtu-LLM, OmniDocBench, olmOCR, dots.ocr, MinerU, PaddleOCR, PSENet for providing model weights, benchmarks and valuable code. We also appreciate everyone's contribution to this open-source project!

📚 Citation

If you find our work useful in your research, please consider citing the following paper:

@article{youtu-parsing,
  title={Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding},
  author={Tencent Youtu Lab},
  year={2026},
  eprint={2601.20430},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2601.20430}, 
}

@article{youtu-vl,
  title={Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision},
  author={Tencent Youtu Lab},
  year={2026},
  eprint={2601.19798},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2601.19798}, 
}

@article{youtu-llm,
  title={Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models},
  author={Tencent Youtu Lab},
  year={2025},
  eprint={2512.24618},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2512.24618}, 
}