Instructions to use zenosai/MonkeyOCRv2-B-Parsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenosai/MonkeyOCRv2-B-Parsing with Transformers:

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

pipe = pipeline("image-text-to-text", model="zenosai/MonkeyOCRv2-B-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("zenosai/MonkeyOCRv2-B-Parsing", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenosai/MonkeyOCRv2-B-Parsing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenosai/MonkeyOCRv2-B-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": "zenosai/MonkeyOCRv2-B-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/zenosai/MonkeyOCRv2-B-Parsing

SGLang

How to use zenosai/MonkeyOCRv2-B-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 "zenosai/MonkeyOCRv2-B-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": "zenosai/MonkeyOCRv2-B-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 "zenosai/MonkeyOCRv2-B-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": "zenosai/MonkeyOCRv2-B-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 zenosai/MonkeyOCRv2-B-Parsing with Docker Model Runner:
```
docker model run hf.co/zenosai/MonkeyOCRv2-B-Parsing
```

zenosai commited on 28 days ago

Commit

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Update README.md

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README.md +8 -5

README.md CHANGED Viewed

@@ -21,8 +21,10 @@ library_name: transformers
 </div>
 ## News
 * `2026.07.21` 📦 We release [MonkeyDoc v2](https://modelscope.cn/datasets/zenosai/MonkeyDocv2), an open multilingual corpus for document-oriented pretraining. We hope it can serve as a shared data foundation for more transparent, reproducible, and fair comparisons in Document AI.
-* `2026.07.11` 🚀 We release [MonkeyOCRv2](https://github.com/Yuliang-Liu/MonkeyOCRv2), including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.
 ## Use MonkeyOCRv2 as a Vision Backbone
@@ -45,11 +47,12 @@ encoder = AutoModel.from_pretrained(
 See the Vision Encoder [Quick Start](https://github.com/Yuliang-Liu/MonkeyOCRv2#vision-encoder) for installation and feature-extraction examples. If you adapt MonkeyOCRv2 to a new task or domain, feel free to open an issue or pull request and share the results.
 ## MonkeyDoc v2
-MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. The open-sourcing of MonkeyDoc v2 is still underway. So far, we have released all 52 million synthetic samples and 35 million real-world samples derived from FinePDF. Additional real-world data from other sources is currently being organized and will be released progressively. You can download the full datset as follows:
 ```bash
 pip install modelscope
 modelscope download --dataset zenosai/MonkeyDocv2 --local_dir ./MonkeyDocv2
 ```
 ## Model Zoo
@@ -249,9 +252,9 @@ python download_model.py -t modelscope -n MonkeyOCRv2-B # or MonkeyOCRv2-S / Mon
 ```bash
 cd vision
 # For MonkeyOCRv2-B and MonkeyOCRv2-S
-python extract_feature.py
 # For MonkeyOCRv2-AS
-python extract_feature_vitae.py
 ```
 ### Document Parsing
@@ -1237,7 +1240,7 @@ This multi-expert agreement strategy reduces model-specific annotation errors an
 - **MonkeyOCR**: https://github.com/Yuliang-Liu/MonkeyOCR
 - **MDPBench**: https://github.com/Yuliang-Liu/MultimodalOCR
 - **MonkeyDoc**: https://modelscope.cn/datasets/zenosai/MonkeyDoc
-- **FinePDF**: https://huggingface.co/datasets/HuggingFaceFW/finepdfs
 ## Citation
 If you use any part of this release — the MonkeyOCRv2 encoders, MonkeyOCRv2-Parsing,

 </div>
 ## News
+* `2026.07.22` 🏆 MonkeyOCRv2-B-Parsing ranks #1 among evaluated open-source models on the official [MDPBench Leaderboard](https://huggingface.co/spaces/Delores-Lin/MDPBench-leaderboard), achieving 83.3 overall across 17 languages, including digital-born and photographed documents.
 * `2026.07.21` 📦 We release [MonkeyDoc v2](https://modelscope.cn/datasets/zenosai/MonkeyDocv2), an open multilingual corpus for document-oriented pretraining. We hope it can serve as a shared data foundation for more transparent, reproducible, and fair comparisons in Document AI.
+* `2026.07.20` 🤗 MonkeyOCRv2 was featured in [Hugging Face Daily Papers](https://huggingface.co/papers/2607.11562) and has entered the Weekly Papers list. Thanks to the community for the support, upvotes, and feedback!
+* `2026.07.14` 🚀 We release [MonkeyOCRv2](https://arxiv.org/abs/2607.11562), including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.
 ## Use MonkeyOCRv2 as a Vision Backbone
 See the Vision Encoder [Quick Start](https://github.com/Yuliang-Liu/MonkeyOCRv2#vision-encoder) for installation and feature-extraction examples. If you adapt MonkeyOCRv2 to a new task or domain, feel free to open an issue or pull request and share the results.
 ## MonkeyDoc v2
+MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. The open-sourcing of MonkeyDoc v2 is still underway. So far, we have released 52 million synthetic samples and 41 million real-world samples. You can download the full datset as follows:
 ```bash
 pip install modelscope
 modelscope download --dataset zenosai/MonkeyDocv2 --local_dir ./MonkeyDocv2
 ```
+After processing and compression, downloading the dataset currently requires approximately 10 TB of disk space. We recommend having at least 11 TB of available storage before starting the download to ensure sufficient space throughout the process.
 ## Model Zoo
 ```bash
 cd vision
 # For MonkeyOCRv2-B and MonkeyOCRv2-S
+python extract_feature.py -m ../model_weight/MonkeyOCRv2-B -i ../images_test/ar.JPEG
 # For MonkeyOCRv2-AS
+python extract_feature_vitae.py -m ../model_weight/MonkeyOCRv2-AS -i ../images_test/ar.JPEG
 ```
 ### Document Parsing
 - **MonkeyOCR**: https://github.com/Yuliang-Liu/MonkeyOCR
 - **MDPBench**: https://github.com/Yuliang-Liu/MultimodalOCR
 - **MonkeyDoc**: https://modelscope.cn/datasets/zenosai/MonkeyDoc
+- **MonkeyDoc v2**: https://modelscope.cn/datasets/zenosai/MonkeyDocv2
 ## Citation
 If you use any part of this release — the MonkeyOCRv2 encoders, MonkeyOCRv2-Parsing,