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 15 hours ago

Commit

4fcf1f1

verified ·

1 Parent(s): 82e57e4

README: show inference code example inline

Browse files

Files changed (1) hide show

README.md +26 -4

README.md CHANGED Viewed

@@ -79,6 +79,8 @@ KORMo-Web-9B outperforms open web agents of similar size in both English and Kor
 Tested with **vLLM 0.23.0** and **transformers 5.12.1**. The model runs on a single GPU (TP=1).
 **1. Install and download**
 ```bash
@@ -103,8 +105,9 @@ python KORMo-Web-9B/examples/agent_playwright.py --model KORMo-Web-9B \
 The agent opens Chromium, prints each action as it goes, and finishes with the model's answer.
-<details>
-<summary><b>Use the model from your own code (one step)</b></summary>
 ```python
 import base64, json
@@ -130,11 +133,30 @@ out = client.chat.completions.create(
     temperature=0.6, top_p=0.95, max_tokens=16384,
     extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True}},
 )
-print(out.choices[0].message.content)   # <think> … </think> <tool_call><function=click> … </tool_call>
 ```
 Coordinates in the tool call are normalized to 0–1000. Convert them to pixels with `x / 1000 × width` and `y / 1000 × height`.
-</details>
 ## How It Works

 Tested with **vLLM 0.23.0** and **transformers 5.12.1**. The model runs on a single GPU (TP=1).
+### Run the agent
 **1. Install and download**
 ```bash
 The agent opens Chromium, prints each action as it goes, and finishes with the model's answer.
+### Inference example (one step)
+Send one screenshot with the request and get back one action:
 ```python
 import base64, json
     temperature=0.6, top_p=0.95, max_tokens=16384,
     extra_body={"top_k": 20, "chat_template_kwargs": {"enable_thinking": True}},
 )
+print(out.choices[0].message.content)
+```
+The output looks like this (illustrative):
+```text
+<think>
+I can see the Google home page with an empty search box in the center. I have not searched yet.
+My plan: 1. Click the search box. 2. Type "KAIST". 3. Press Enter and open the official site.
+</think>
+<tool_call>
+<function=click>
+<parameter=x>
+500
+</parameter>
+<parameter=y>
+427
+</parameter>
+</function>
+</tool_call>
 ```
 Coordinates in the tool call are normalized to 0–1000. Convert them to pixels with `x / 1000 × width` and `y / 1000 × height`.
 ## How It Works