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
```

Update pipeline tag to any-to-any and add paper link

by nielsr HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+22

-21

Files changed (1) hide show

README.md +22 -21

README.md CHANGED Viewed

@@ -1,31 +1,31 @@
 ---
 license: other
 license_name: apache-2.0-and-sam-license
 license_link: LICENSE
-library_name: transformers
-pipeline_tag: image-text-to-text
 tags:
-  - multimodal
-  - scientific
-  - protein
-  - rna
-  - dna
-  - molecule
-  - weather
-  - medical-imaging
-base_model:
-  - Qwen/Qwen3-VL-8B-Instruct
-extra_gated_heading: You need to agree to Meta's SAM License to use the medical-image segmentation weights
-extra_gated_description: >-
-  The bulk of this model is Apache-2.0. The medical-image segmentation branch
-  embeds SAM 3 weights, which are governed by Meta's SAM License (see
-  SAM_LICENSE.txt). By accessing these weights you agree to that license,
-  including its acceptable-use restrictions.
 ---
 <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](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB/blob/main/docs/MKB.pdf) &nbsp;•&nbsp; [⚖️ License: Apache-2.0 + SAM License](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB/blob/main/LICENSE)
 </div>
@@ -147,7 +147,8 @@ export PYTHONPATH=$PWD/code
 python code/inference.py --model_path model --greedy --max_new_tokens 64 \
   --rna "GGATGCGATCATGTCTGCACTAACACACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGGGAGTAGTACTAGGATGGGCGACCCCTTAGGAAGTACTCGTGTTGCATCCC" \
   --system "You are a non-coding RNA family classifier. Output only the family name, no other text." \
-  --prompt $'<rna>\nWhich family does this non-coding RNA sequence belong to?'
 ```
 All weights are contained in `model.safetensors`: the scientific
@@ -179,4 +180,4 @@ See `THIRD_PARTY_LICENSES.md` / `NOTICE` for the full third-party breakdown.
   year   = {2026},
   note   = {https://huggingface.co/sais-org/MKB}
 }
-```

 ---
+base_model:
+- Qwen/Qwen3-VL-8B-Instruct
+library_name: transformers
 license: other
 license_name: apache-2.0-and-sam-license
 license_link: LICENSE
+pipeline_tag: any-to-any
 tags:
+- multimodal
+- scientific
+- protein
+- rna
+- dna
+- molecule
+- weather
+- medical-imaging
+extra_gated_heading: You need to agree to Meta's SAM License to use the medical-image
+  segmentation weights
+extra_gated_description: The bulk of this model is Apache-2.0. The medical-image segmentation
+  branch embeds SAM 3 weights, which are governed by Meta's SAM License (see SAM_LICENSE.txt).
+  By accessing these weights you agree to that license, including its acceptable-use
+  restrictions.
 ---
 <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](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB/blob/main/docs/MKB.pdf) &nbsp;•&nbsp; [📄 Paper](https://huggingface.co/papers/2607.20557) &nbsp;•&nbsp; [⚖️ License: Apache-2.0 + SAM License](https://github.com/Shanghai-Academy-of-AI-For-Science/MKB/blob/main/LICENSE)
 </div>
 python code/inference.py --model_path model --greedy --max_new_tokens 64 \
   --rna "GGATGCGATCATGTCTGCACTAACACACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGGGAGTAGTACTAGGATGGGCGACCCCTTAGGAAGTACTCGTGTTGCATCCC" \
   --system "You are a non-coding RNA family classifier. Output only the family name, no other text." \
+  --prompt $'<rna>
+Which family does this non-coding RNA sequence belong to?'
 ```
 All weights are contained in `model.safetensors`: the scientific
   year   = {2026},
   note   = {https://huggingface.co/sais-org/MKB}
 }
+```