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 23 days ago

Commit

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@@ -21,9 +21,9 @@ library_name: transformers
 </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
@@ -264,27 +264,44 @@ Install vLLM following its [official guide](https://docs.vllm.ai/en/v0.11.2/gett
 conda create -n MonkeyOCRv2Parsing python=3.10
 conda activate MonkeyOCRv2Parsing
 pip install uv
-uv pip install vllm==0.11.2 --torch-backend=auto -i https://pypi.tuna.tsinghua.edu.cn/simple requests
 pip install -r parsing/requirements.txt
 ```
 #### 2. Download Model Weights
 Download our model from Huggingface.
 ```bash
 python download_model.py -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
 ```
 You can also download our model from ModelScope.
 ```bash
 pip install modelscope
 python download_model.py -t modelscope -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
 ```
 #### 3. vLLM Serving
 You should start a vLLM service before parsing documents:
 ```bash
 cd parsing
-python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing -p 8888
 # Show help messages
 python serve.py -h
 ```
@@ -1242,6 +1259,9 @@ This multi-expert agreement strategy reduces model-specific annotation errors an
 - **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,
 MonkeyOCRv2-Und, the MDPBench benchmark, or the MonkeyDoc v2 dataset — please cite:
@@ -1259,4 +1279,4 @@ MonkeyOCRv2-Und, the MDPBench benchmark, or the MonkeyDoc v2 dataset — please
 ```
 ## Copyright
-We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. The models are  are released under the [Apache License 2.0](LICENSE) and are free for both research and commercial use. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.

 </div>
 ## News
+* `2026.07.24` ⚡ We released [MonkeyOCRv2-B-Parsing-DFlash](https://huggingface.co/zenosai/MonkeyOCRv2-B-Parsing-DFlash), enabling vLLM serving with DFlash for up to 2× faster inference.
 * `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.
 * `2026.07.14` 🚀 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
 conda create -n MonkeyOCRv2Parsing python=3.10
 conda activate MonkeyOCRv2Parsing
 pip install uv
+uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.25.1/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 -i https://pypi.tuna.tsinghua.edu.cn/simple
 pip install -r parsing/requirements.txt
 ```
+To use DFlash for faster inference, **vLLM 0.25.1** is required, which depends on **CUDA 12.9 or later**.
+If your system does not support CUDA 12.9, you can instead install **vLLM 0.11.2** (without DFlash support) by running:
+```bash
+uv pip install vllm==0.11.2 --torch-backend=auto -i https://pypi.tuna.tsinghua.edu.cn/simple requests
+```
+Inference will still work normally, but DFlash acceleration will not be available.
 #### 2. Download Model Weights
 Download our model from Huggingface.
 ```bash
 python download_model.py -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
+# use DFlash for faster inference, support MonkeyOCRv2-B-Parsing only for now
+python download_model.py -n MonkeyOCRv2-B-Parsing-DFlash
 ```
 You can also download our model from ModelScope.
 ```bash
 pip install modelscope
 python download_model.py -t modelscope -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
+# use DFlash for faster inference, support MonkeyOCRv2-B-Parsing only for now
+python download_model.py -n MonkeyOCRv2-B-Parsing-DFlash
 ```
 #### 3. vLLM Serving
 You should start a vLLM service before parsing documents:
 ```bash
 cd parsing
+# Serve with DFlash for faster inference
+python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing -d ../model_weight/MonkeyOCRv2-B-Parsing-DFlash -p 8888
+# Serve without DFlash
+python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing  -p 8888
 # Show help messages
 python serve.py -h
 ```
 - **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,
 MonkeyOCRv2-Und, the MDPBench benchmark, or the MonkeyDoc v2 dataset — please cite:
 ```
 ## Copyright
+Share reproductions, integrations, and failure cases in [GitHub Discussions](https://github.com/Yuliang-Liu/MonkeyOCRv2/discussions/13). We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. The models are  are released under the [Apache License 2.0](LICENSE) and are free for both research and commercial use. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.