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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Yinsongliu commited on Jan 27

Commit

0d8c591

1 Parent(s): c13c3aa

docs: Update README and refresh asset images

Browse files

Files changed (12) hide show

README.md +1 -14
assets/art.png +2 -2
assets/chart.png +2 -2
assets/chart2.png +2 -2
assets/formula.png +2 -2
assets/handwriting.png +2 -2
assets/hierarchical.png +0 -3
assets/hierarchical2.png +0 -3
assets/printed.png +2 -2
assets/seal.png +0 -3
assets/wired.png +2 -2
assets/wireless.png +2 -2

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ base_model_relation: finetune
 # <img src="assets/youtu-parsing-logo.png" alt="Youtu-Parsing Logo" height="100px">
-[📃 License](https://huggingface.co/tencent/Youtu-Parsing/blob/main/LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-parsing) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
 </div>
@@ -128,19 +128,6 @@ parser.parse_file(input_path=image_path, output_dir=output_dir)
 <img src="./assets/chart2.png" width="800"/>
 </div>
-### Seal Recognition
-<div align="center">
-<img src="./assets/seal.png" width="800"/>
-</div>
-### Hierarchical Structure Analysis
-<div align="center">
-<img src="./assets/hierarchical.png" width="800"/>
-</div>
-<div align="center">
-<img src="./assets/hierarchical2.png" width="800"/>
-</div>
 ## 🤝 Acknowledgements
 We would like to thank [Youtu-LLM](https://github.com/TencentCloudADP/youtu-tip/tree/master/youtu-llm), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), [olmOCR](https://github.com/allenai/olmocr), [dots.ocr](https://github.com/rednote-hilab/dots.ocr), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [PSENet](https://github.com/whai362/PSENet) for providing model weights, benchmarks and valuable code. We also appreciate everyone's contribution to this open-source project!

 # <img src="assets/youtu-parsing-logo.png" alt="Youtu-Parsing Logo" height="100px">
+[📃 License](https://huggingface.co/tencent/Youtu-Parsing/blob/main/LICENSE.txt) • [👨‍💻 Code](https://github.com/TencentCloudADP/youtu-parsing) • [🖥️ Demo](https://huggingface.co/spaces/Tencent/Youtu-Parsing) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
 </div>
 <img src="./assets/chart2.png" width="800"/>
 </div>
 ## 🤝 Acknowledgements
 We would like to thank [Youtu-LLM](https://github.com/TencentCloudADP/youtu-tip/tree/master/youtu-llm), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), [olmOCR](https://github.com/allenai/olmocr), [dots.ocr](https://github.com/rednote-hilab/dots.ocr), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [PSENet](https://github.com/whai362/PSENet) for providing model weights, benchmarks and valuable code. We also appreciate everyone's contribution to this open-source project!