Instructions to use KORMo-VL/KORMo-Web-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KORMo-VL/KORMo-Web-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="KORMo-VL/KORMo-Web-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("KORMo-VL/KORMo-Web-9B")
model = AutoModelForMultimodalLM.from_pretrained("KORMo-VL/KORMo-Web-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KORMo-VL/KORMo-Web-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KORMo-VL/KORMo-Web-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KORMo-VL/KORMo-Web-9B",
		"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/KORMo-VL/KORMo-Web-9B

SGLang

How to use KORMo-VL/KORMo-Web-9B 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 "KORMo-VL/KORMo-Web-9B" \
    --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": "KORMo-VL/KORMo-Web-9B",
		"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 "KORMo-VL/KORMo-Web-9B" \
        --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": "KORMo-VL/KORMo-Web-9B",
		"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 KORMo-VL/KORMo-Web-9B with Docker Model Runner:
```
docker model run hf.co/KORMo-VL/KORMo-Web-9B
```

jungsin3 commited on about 2 hours ago

Commit

bc2bf7b

verified ·

1 Parent(s): 207e0bb

Add demo video

Browse files

Files changed (3) hide show

.gitattributes +1 -0
README.md +6 -0
assets/demo_movie_booking.mp4 +3 -0

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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 tokenizer.json filter=lfs diff=lfs merge=lfs -text

 *.zst filter=lfs diff=lfs merge=lfs -text
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+assets/demo_movie_booking.mp4 filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -48,6 +48,12 @@ tags:
 On **Online-Mind2Web** it reaches **51.0% in English** and **48.1% in Korean**, which is **+26.3 / +31.4 points** over the base Qwen3.5-9B.
 ## Performance
 All models below ran **the same 612 live-website tasks** (300 English, 312 Korean), each with its own prompt and action format, and were scored by **the same LLM judge**.

 On **Online-Mind2Web** it reaches **51.0% in English** and **48.1% in Korean**, which is **+26.3 / +31.4 points** over the base Qwen3.5-9B.
+## Demo
+<video controls muted playsinline style="width: 100%; border-radius: 8px;" src="https://huggingface.co/KORMo-VL/KORMo-Web-9B/resolve/main/assets/demo_movie_booking.mp4"></video>
+KORMo-Web-9B books a movie ticket on the live CGV website from a single Korean request: it logs in, picks the theater, date and showtime, selects seats and goes to the payment step.
 ## Performance
 All models below ran **the same 612 live-website tasks** (300 English, 312 Korean), each with its own prompt and action format, and were scored by **the same LLM judge**.

assets/demo_movie_booking.mp4 ADDED Viewed

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+oid sha256:fd6ea2cbbe840b4beb354b8a6e579fc46f3e775c3f03fe800d66d15c20f10760
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