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

Libraries

How to use zenosai/MonkeyOCRv2-S-Und with Transformers:

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

pipe = pipeline("image-text-to-text", model="zenosai/MonkeyOCRv2-S-Und", 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-S-Und", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenosai/MonkeyOCRv2-S-Und with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zenosai/MonkeyOCRv2-S-Und"
# 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-S-Und",
		"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-S-Und

SGLang

How to use zenosai/MonkeyOCRv2-S-Und 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-S-Und" \
    --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-S-Und",
		"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-S-Und" \
        --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-S-Und",
		"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-S-Und with Docker Model Runner:
```
docker model run hf.co/zenosai/MonkeyOCRv2-S-Und
```

Add library_name metadata to model card

by nielsr HF Staff - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,9 +1,11 @@
 ---
-license: apache-2.0
 datasets:
 - zenosai/MonkeyDocv2
 pipeline_tag: image-text-to-text
 ---
 <div align="center" xmlns="http://www.w3.org/1999/html">
 <h2>
 <b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
@@ -533,7 +535,7 @@ Our model supports robust document parsing in real-world scenarios across 17 lan
 #### 3. Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of [MMOCR](https://github.com/open-mmlab/mmocr) and [DPText-DETR](https://github.com/ymy-k/DPText-DETR).
 <p align="center">
-  <img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/overview.png?raw=true" width="600"/>
 </p>
@@ -1189,4 +1191,4 @@ This multi-expert agreement strategy reduces model-specific annotation errors an
 - **Qwen3**: https://github.com/QwenLM/Qwen3
 ## Copyright
-We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. Note: This model is intended for academic research and non-commercial use only. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.

 ---
 datasets:
 - zenosai/MonkeyDocv2
+license: apache-2.0
 pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 <div align="center" xmlns="http://www.w3.org/1999/html">
 <h2>
 <b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
 #### 3. Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of [MMOCR](https://github.com/open-mmlab/mmocr) and [DPText-DETR](https://github.com/ymy-k/DPText-DETR).
 <p align="center">
+  <img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/text_detection.png" width="600"/>
 </p>
 - **Qwen3**: https://github.com/QwenLM/Qwen3
 ## Copyright
+We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. Note: This model is intended for academic research and non-commercial use only. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.