Instructions to use sais-org/MKB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sais-org/MKB with Transformers:

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

pipe = pipeline("image-text-to-text", model="sais-org/MKB")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sais-org/MKB")
model = AutoModelForMultimodalLM.from_pretrained("sais-org/MKB", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sais-org/MKB with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sais-org/MKB"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sais-org/MKB",
		"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/sais-org/MKB

SGLang

How to use sais-org/MKB 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 "sais-org/MKB" \
    --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": "sais-org/MKB",
		"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 "sais-org/MKB" \
        --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": "sais-org/MKB",
		"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 sais-org/MKB with Docker Model Runner:
```
docker model run hf.co/sais-org/MKB
```

Descartes commited on 26 days ago

Commit

8dd1215

verified ·

1 Parent(s): 3fa9c43

Rename to Monkey King Bang (神珍); ~11B total params

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -25,11 +25,11 @@ extra_gated_description: >-
 <div align="center">
-[🤗 Model](https://huggingface.co/sais-org/ShenZhen) &nbsp;•&nbsp; [💻 GitHub](https://github.com/Shanghai-Academy-of-AI-For-Science/ShenZhen) &nbsp;•&nbsp; [📜 Technical Report (coming soon)](#) &nbsp;•&nbsp; [⚖️ License: Apache-2.0 + SAM License](https://github.com/Shanghai-Academy-of-AI-For-Science/ShenZhen/blob/main/LICENSE)
 </div>
-# 神珍 (ShenZhen)
 **神珍 is a unified scientific multimodal foundation model** that
 supports scientific **understanding and generation** across Earth science,
@@ -139,9 +139,9 @@ nine imaging modalities, versus six modality-native segmentation specialists.
 Runs via the accompanying code repository (custom multimodal architecture).
 ```bash
-git clone https://github.com/Shanghai-Academy-of-AI-For-Science/ShenZhen && cd ShenZhen
 pip install -r requirements.txt          # Python 3.10; transformers==5.0.0
-hf download sais-org/ShenZhen --local-dir ./model
 export PYTHONPATH=$PWD/code
 python code/inference.py --model_path model --greedy --max_new_tokens 64 \
@@ -173,10 +173,10 @@ See `THIRD_PARTY_LICENSES.md` / `NOTICE` for the full third-party breakdown.
 ## Citation
 ```bibtex
-@misc{shenzhen2026,
-  title  = {ShenZhen (神珍): A Unified Scientific Multimodal Foundation Model},
   author = {Hesen Chen and Xinyu Su and Xiaomeng Yang and Yuetan Lin and Zixiong Yang and Zhiyu Tan and Hao Li},
   year   = {2026},
-  note   = {https://huggingface.co/sais-org/ShenZhen}
 }
 ```

 <div align="center">
+[🤗 Model](https://huggingface.co/sais-org/MKB) &nbsp;•&nbsp; [💻 GitHub](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB) &nbsp;•&nbsp; [📜 Technical Report (coming soon)](#) &nbsp;•&nbsp; [⚖️ License: Apache-2.0 + SAM License](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB/blob/main/LICENSE)
 </div>
+# 神珍 · Monkey King Bang (MKB)
 **神珍 is a unified scientific multimodal foundation model** that
 supports scientific **understanding and generation** across Earth science,
 Runs via the accompanying code repository (custom multimodal architecture).
 ```bash
+git clone https://github.com/Shanghai-Academy-of-AI-For-Science/MKB && cd MKB
 pip install -r requirements.txt          # Python 3.10; transformers==5.0.0
+hf download sais-org/MKB --local-dir ./model
 export PYTHONPATH=$PWD/code
 python code/inference.py --model_path model --greedy --max_new_tokens 64 \
 ## Citation
 ```bibtex
+@misc{mkb2026,
+  title  = {MonkeyKing Bang: A Unified Scientific Multimodal Foundation Model},
   author = {Hesen Chen and Xinyu Su and Xiaomeng Yang and Yuetan Lin and Zixiong Yang and Zhiyu Tan and Hao Li},
   year   = {2026},
+  note   = {https://huggingface.co/sais-org/MKB}
 }
 ```