Instructions to use KORMo-VL/KORMo-Web-10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KORMo-VL/KORMo-Web-10B 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-10B")
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-10B")
model = AutoModelForMultimodalLM.from_pretrained("KORMo-VL/KORMo-Web-10B", 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-10B 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-10B"
# 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-10B",
		"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-10B

SGLang

How to use KORMo-VL/KORMo-Web-10B 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-10B" \
    --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-10B",
		"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-10B" \
        --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-10B",
		"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-10B with Docker Model Runner:
```
docker model run hf.co/KORMo-VL/KORMo-Web-10B
```

KORMo-Web-10B / README.md

jungsin3

README: use a real KORMo-Web-10B output in the example

5636a20 verified 26 minutes ago

preview code

Raw History Blame Contribute Delete

11.2 kB

---
license: apache-2.0
base_model:
- KORMo-VL/KORMo-VL
language:
- ko
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- gui-agent
- web-agent
- computer-use
- browser
- korean
---
<style>
#kormo-web-card { font-size: 16px; line-height: 1.6; overflow-wrap: break-word; }
#kormo-web-card h1 { margin: 0 0 12px; font-size: 30px; line-height: 1.25; letter-spacing: -0.018em; }
#kormo-web-card h2 { margin: 30px 0 10px; font-size: 22px; line-height: 1.3; letter-spacing: -0.012em; }
#kormo-web-card h3 { margin: 22px 0 8px; font-size: 17px; font-weight: 700; line-height: 1.4; }
#kormo-web-card h2 + h3 { margin-top: 14px; }
#kormo-web-card p { margin: 0 0 10px; }
#kormo-web-card ul, #kormo-web-card ol { margin: 6px 0 12px; padding-left: 22px; }
#kormo-web-card li { margin: 4px 0; line-height: 1.55; }
#kormo-web-card table { width: 100%; margin: 6px 0 14px; border-collapse: collapse; font-size: 14px; line-height: 1.45; font-variant-numeric: tabular-nums; }
#kormo-web-card th, #kormo-web-card td { padding: 7px 10px; vertical-align: middle; }
#kormo-web-card blockquote { margin: 12px 0 16px; padding: 0 0 0 14px; font-size: 15px; }
#kormo-web-card blockquote p { margin: 0; }
#kormo-web-card pre { margin: 10px 0 14px; padding: 14px 16px; overflow-x: auto; line-height: 1.5; }
#kormo-web-card pre code { font-size: 13px; }
#kormo-web-card > p:has(> em:only-child) { margin: 4px 0 14px; font-size: 13px; }
#kormo-web-card .pills { margin: 4px 0 18px; }
#kormo-web-card .pill { display: inline-block; margin: 0 6px 6px 0; padding: 3px 10px; border-radius: 999px; border: 1px solid rgba(127,127,127,.35); font-size: 13px; }
@media (max-width: 640px) {
  #kormo-web-card h1 { font-size: 26px; }
  #kormo-web-card table { font-size: 13px; }
  #kormo-web-card th, #kormo-web-card td { padding: 7px 6px; }
}
</style>

<div id="kormo-web-card">

# KORMo-Web-10B

**KORMo-Web-10B** is a Korean-first web agent built entirely on the **KORMo** model family, which KAIST MLP Lab trains **from scratch**. It reads a screenshot, thinks, and takes one browser action at a time (click, type, scroll, move between tabs and sites) until the task is done.

Among the open web agents we compared, it scores the **highest in Korean (30.1%)** on Online-Mind2Web, and **29.0% in English**.

## From a Korean LLM to a Korean web agent

KORMo-Web-10B is the third step of one Korean lineage, not a fine-tune of a foreign base model:

| Step | Model | What it adds |
|---|---|---|
| 1 | [KORMo-10B](https://huggingface.co/KORMo-Team/KORMo-10B-sft) | Korean–English LLM trained from scratch, with its own Korean tokenizer |
| 2 | [KORMo-VL](https://huggingface.co/KORMo-VL/KORMo-VL) | Vision: SigLIP encoder + KORMo-10B, trained on Korean document and UI images |
| 3 | **KORMo-Web-10B** | Web agent: large-scale web understanding data and browser trajectories, in Korean and English |

Because every stage was trained with Korean as a first language, the model handles Korean pages, Korean UI terms and natural Korean requests directly.

## 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**.

| Model | Size | Online-Mind2Web EN | Online-Mind2Web KO |
|---|---:|---:|---:|
| Qwen3.5-9B (base, no fine-tuning) | 9B | 24.7 | 16.7 |
| Fara-1.5-4B | 4B | 20.7 | 7.7 |
| UI-TARS-1.5-7B | 7B | 22.3 | 20.2 |
| OpenWebRL-4B | 4B | 32.7 | 15.4 |
| **KORMo-Web-10B (ours)** | **11B** | **29.0** | **30.1** |
| GPT-6-luna (proprietary) | – | 63.0 | 65.7 |
| Gemini-3.8-flash (proprietary) | – | 74.0 | 80.1 |


Success rate (%). The denominator is always the full task count, so crashed or empty runs count as failures.

KORMo-Web-10B has the best Korean score among the open web agents above, by about 10 points.

## Why KORMo-Web-10B

- **Korean from the ground up.** Built on a Korean LLM trained from scratch, not adapted from an English-first model.
- **Korean and English, natural requests.** Handles short commands as well as long, conversational instructions in either language.
- **Screenshot-only.** It needs no DOM or accessibility tree; it acts on what is on screen and checks each action's result on the next screenshot.
- **Thinks, then acts.** A short reasoning step precedes every action, so its behaviour is easy to inspect.
- **Open and ready to run.** Weights, model code for transformers 5, a vLLM path, a reference browser harness and the exact inference settings are all included.

## Quickstart

Tested with **transformers 5.12.1** and **vLLM 0.23.0**. The model runs on a single GPU.

transformers does not ship the KORMo text model, so this repo includes it (`_modeling_kormo.py`) together with a small loader, `kormo_load.py`:

- `kormo_load.register(path)` registers the KORMo text model. Call it **before** loading the processor or the model.
- `kormo_load.patch_vision(model)` feeds the SigLIP features *after* post-layernorm to the projector, as in training. Without it the model still runs but misreads screenshots.

### Inference example (one step, transformers)

```bash
pip install -U "transformers>=5.12" torch pillow huggingface_hub
huggingface-cli download KORMo-VL/KORMo-Web-10B --local-dir KORMo-Web-10B
```

```python
import json, sys, torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor
M = "KORMo-Web-10B"
sys.path.insert(0, M)
import kormo_load
kormo_load.register(M)
processor = AutoProcessor.from_pretrained(M)
model = AutoModelForImageTextToText.from_pretrained(M, dtype=torch.bfloat16).to("cuda").eval()
kormo_load.patch_vision(model)
SYSTEM = open(f"{M}/system_prompt.md").read().strip()
TOOLS = json.load(open(f"{M}/tools.json"))
obs = ("<observation>\nscreen size: 1280 x 720\nCurrent tab: 0\nAvailable tabs:\n"
       "- Tab 0 (active): https://www.aladin.co.kr/\n\nurl: https://www.aladin.co.kr/\nscreenshot:\n")
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": [
        {"type": "text", "text": "<user_request>\n알라딘 사이트에서 '해커스 토익 RC'를 검색한 뒤 가장 먼저 표시되는 도서의 출간일을 알려줘.\n</user_request>\n" + obs},
        {"type": "image", "image": Image.open("screen.png").convert("RGB")},
        {"type": "text", "text": "\n</observation>"},
    ]},
]
inputs = processor.apply_chat_template(messages, tools=TOOLS, add_generation_prompt=True,
                                       tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=16384, do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
```

A real first step from our Online-Mind2Web run (Aladin home page):

```text
<think>I see the Aladin homepage, with the search field labeled “통합검색” near the top. I have not yet searched for the book, so I still need to enter “해커스 토익 RC,” inspect the first result’s publication date, and report that date. My plan:
1. Click the search field
2. Enter “해커스 토익 RC” and submit the search
3. Read the publication date of the first listed book
This click targets the visible search field and is a safe, relevant first action.
</think>
<tool_call>
<function=click>
<parameter=x>
482
</parameter>
<parameter=y>
212
</parameter>
</function>
</tool_call>
```

Coordinates are normalized to 0–1000. Convert them to pixels with `x / 1000 × width` and `y / 1000 × height`.

### Run the agent (vLLM)

vLLM does not include the KORMo architecture either. The KORMo text layers have the Llama structure, so `vllm/convert_to_vllm.py` renames two parameter names and writes a folder vLLM loads natively. `vllm/sitecustomize.py` turns SigLIP post-layernorm back on inside vLLM.

**1. Convert (once)**

```bash
pip install -U "vllm>=0.23.0" openai playwright safetensors
playwright install chromium
python KORMo-Web-10B/vllm/convert_to_vllm.py KORMo-Web-10B KORMo-Web-10B-vllm
```

**2. Start the model server**

```bash
PYTHONPATH=$PWD/KORMo-Web-10B/vllm vllm serve ./KORMo-Web-10B-vllm --served-model-name KORMo-Web-10B \
  --max-model-len 65536 --limit-mm-per-prompt '{"image":1,"video":0}'
```

Check that the server log prints `[kormo-vllm] SigLIP post_layernorm enabled`.

**3. Run a browser task**

```bash
python KORMo-Web-10B/examples/agent_playwright.py --model KORMo-Web-10B \
  --task "다나와에서 50만 원 이하, 10인치 이상 태블릿 3개를 찾아서 가격을 알려줘"
```

The agent opens Chromium, prints each action as it goes, and finishes with the model's answer.

## How It Works

```
user       <user_request> task </user_request>  + observation (tabs, url, screenshot)
assistant  <think> what I see · what is done · plan </think>  <tool_call> one action </tool_call>
tool       action result  + new observation (newest screenshot only)
…          repeat until finished(answer)
```

- **Action space:** 22 browser tools: click, type, hotkey, scroll, drag, goto, tab control, `finished`, and others ([`tools.json`](tools.json)).
- **Memory:** the full text history of thoughts, actions and observations is kept. Only the current screenshot is sent.
- **Recommended settings:** temperature 0.6, top_p 0.95, top_k 20, thinking on, up to 16k tokens per step and 50 steps. These are already set in `generation_config.json`.

The exact message format, parsing rules and feedback strings are in **[HARNESS.md](HARNESS.md)**. Follow it if you build your own harness.

## Evaluation Details

| | |
|---|---|
| Tasks | Online-Mind2Web: 300 English, 312 Korean |
| Judge | GPT-6 |

## Files

| File | Description |
|---|---|
| `model.safetensors` · `config.json` | Weights and config (LLaVA-OneVision: SigLIP + KORMo-10B text model) |
| `_configuration_kormo.py` · `_modeling_kormo.py` | KORMo text model code for transformers 5 |
| `kormo_load.py` | `register()` and `patch_vision()` |
| `chat_template.jinja` | Chat template (XML tool calls, thinking on) |
| `system_prompt.md` · `tools.json` | System prompt and 22-tool schema |
| `HARNESS.md` | Full harness specification |
| `examples/agent_playwright.py` | Reference browser agent (Playwright + OpenAI-compatible API). It does not execute `upload`. |
| `vllm/convert_to_vllm.py` · `vllm/sitecustomize.py` | Convert for vLLM and the post-layernorm fix |
| `generation_config.json` · `*processor_config.json` · `tokenizer*.json` · `special_tokens_map.json` | Sampling, processor and tokenizer configs |

## 한국어 요약

**KORMo-Web-10B**는 KAIST MLP 연구실이 처음부터 학습한 한국어 모델 **KORMo-10B → KORMo-VL**을 이어 만든 한국어 우선 웹 에이전트입니다. 스크린샷만 보고 한국어·영어 웹 작업을 수행합니다.

- **Online-Mind2Web:** EN 29.0, KO 30.1
- 비교한 공개 웹 에이전트 중 한국어 점수가 가장 높습니다.

## Acknowledgements

Built on **[KORMo-VL](https://huggingface.co/KORMo-VL/KORMo-VL)** and **[KORMo-10B](https://huggingface.co/KORMo-Team/KORMo-10B-sft)** by KAIST MLP Lab. Evaluated on **Online-Mind2Web** and its Korean counterpart.

</div>

	---
	license: apache-2.0
	base_model:
	- KORMo-VL/KORMo-VL
	language:
	- ko
	- en
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- gui-agent
	- web-agent
	- computer-use
	- browser
	- korean
	---

	<style>
	#kormo-web-card { font-size: 16px; line-height: 1.6; overflow-wrap: break-word; }
	#kormo-web-card h1 { margin: 0 0 12px; font-size: 30px; line-height: 1.25; letter-spacing: -0.018em; }
	#kormo-web-card h2 { margin: 30px 0 10px; font-size: 22px; line-height: 1.3; letter-spacing: -0.012em; }
	#kormo-web-card h3 { margin: 22px 0 8px; font-size: 17px; font-weight: 700; line-height: 1.4; }
	#kormo-web-card h2 + h3 { margin-top: 14px; }
	#kormo-web-card p { margin: 0 0 10px; }
	#kormo-web-card ul, #kormo-web-card ol { margin: 6px 0 12px; padding-left: 22px; }
	#kormo-web-card li { margin: 4px 0; line-height: 1.55; }
	#kormo-web-card table { width: 100%; margin: 6px 0 14px; border-collapse: collapse; font-size: 14px; line-height: 1.45; font-variant-numeric: tabular-nums; }
	#kormo-web-card th, #kormo-web-card td { padding: 7px 10px; vertical-align: middle; }
	#kormo-web-card blockquote { margin: 12px 0 16px; padding: 0 0 0 14px; font-size: 15px; }
	#kormo-web-card blockquote p { margin: 0; }
	#kormo-web-card pre { margin: 10px 0 14px; padding: 14px 16px; overflow-x: auto; line-height: 1.5; }
	#kormo-web-card pre code { font-size: 13px; }
	#kormo-web-card > p:has(> em:only-child) { margin: 4px 0 14px; font-size: 13px; }
	#kormo-web-card .pills { margin: 4px 0 18px; }
	#kormo-web-card .pill { display: inline-block; margin: 0 6px 6px 0; padding: 3px 10px; border-radius: 999px; border: 1px solid rgba(127,127,127,.35); font-size: 13px; }
	@media (max-width: 640px) {
	#kormo-web-card h1 { font-size: 26px; }
	#kormo-web-card table { font-size: 13px; }
	#kormo-web-card th, #kormo-web-card td { padding: 7px 6px; }
	}
	</style>

	<div id="kormo-web-card">

	# KORMo-Web-10B

	KORMo-Web-10B is a Korean-first web agent built entirely on the KORMo model family, which KAIST MLP Lab trains from scratch. It reads a screenshot, thinks, and takes one browser action at a time (click, type, scroll, move between tabs and sites) until the task is done.

	Among the open web agents we compared, it scores the highest in Korean (30.1%) on Online-Mind2Web, and 29.0% in English.

	## From a Korean LLM to a Korean web agent

	KORMo-Web-10B is the third step of one Korean lineage, not a fine-tune of a foreign base model:

	\| Step \| Model \| What it adds \|
	\|---\|---\|---\|
	\| 1 \| [KORMo-10B](https://huggingface.co/KORMo-Team/KORMo-10B-sft) \| Korean–English LLM trained from scratch, with its own Korean tokenizer \|
	\| 2 \| [KORMo-VL](https://huggingface.co/KORMo-VL/KORMo-VL) \| Vision: SigLIP encoder + KORMo-10B, trained on Korean document and UI images \|
	\| 3 \| KORMo-Web-10B \| Web agent: large-scale web understanding data and browser trajectories, in Korean and English \|

	Because every stage was trained with Korean as a first language, the model handles Korean pages, Korean UI terms and natural Korean requests directly.

	## 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.

	\| Model \| Size \| Online-Mind2Web EN \| Online-Mind2Web KO \|
	\|---\|---:\|---:\|---:\|
	\| Qwen3.5-9B (base, no fine-tuning) \| 9B \| 24.7 \| 16.7 \|
	\| Fara-1.5-4B \| 4B \| 20.7 \| 7.7 \|
	\| UI-TARS-1.5-7B \| 7B \| 22.3 \| 20.2 \|
	\| OpenWebRL-4B \| 4B \| 32.7 \| 15.4 \|
	\| KORMo-Web-10B (ours) \| 11B \| 29.0 \| 30.1 \|
	\| GPT-6-luna (proprietary) \| – \| 63.0 \| 65.7 \|
	\| Gemini-3.8-flash (proprietary) \| – \| 74.0 \| 80.1 \|


	Success rate (%). The denominator is always the full task count, so crashed or empty runs count as failures.

	KORMo-Web-10B has the best Korean score among the open web agents above, by about 10 points.

	## Why KORMo-Web-10B

	- Korean from the ground up. Built on a Korean LLM trained from scratch, not adapted from an English-first model.
	- Korean and English, natural requests. Handles short commands as well as long, conversational instructions in either language.
	- Screenshot-only. It needs no DOM or accessibility tree; it acts on what is on screen and checks each action's result on the next screenshot.
	- Thinks, then acts. A short reasoning step precedes every action, so its behaviour is easy to inspect.
	- Open and ready to run. Weights, model code for transformers 5, a vLLM path, a reference browser harness and the exact inference settings are all included.

	## Quickstart

	Tested with transformers 5.12.1 and vLLM 0.23.0. The model runs on a single GPU.

	transformers does not ship the KORMo text model, so this repo includes it (`_modeling_kormo.py`) together with a small loader, `kormo_load.py`:

	- `kormo_load.register(path)` registers the KORMo text model. Call it before loading the processor or the model.
	- `kormo_load.patch_vision(model)` feeds the SigLIP features after post-layernorm to the projector, as in training. Without it the model still runs but misreads screenshots.

	### Inference example (one step, transformers)

	```bash
	pip install -U "transformers>=5.12" torch pillow huggingface_hub
	huggingface-cli download KORMo-VL/KORMo-Web-10B --local-dir KORMo-Web-10B
	```

	```python
	import json, sys, torch
	from PIL import Image
	from transformers import AutoModelForImageTextToText, AutoProcessor

	M = "KORMo-Web-10B"
	sys.path.insert(0, M)
	import kormo_load
	kormo_load.register(M)
	processor = AutoProcessor.from_pretrained(M)
	model = AutoModelForImageTextToText.from_pretrained(M, dtype=torch.bfloat16).to("cuda").eval()
	kormo_load.patch_vision(model)

	SYSTEM = open(f"{M}/system_prompt.md").read().strip()
	TOOLS = json.load(open(f"{M}/tools.json"))
	obs = ("<observation>\nscreen size: 1280 x 720\nCurrent tab: 0\nAvailable tabs:\n"
	"- Tab 0 (active): https://www.aladin.co.kr/\n\nurl: https://www.aladin.co.kr/\nscreenshot:\n")
	messages = [
	{"role": "system", "content": SYSTEM},
	{"role": "user", "content": [
	{"type": "text", "text": "<user_request>\n알라딘 사이트에서 '해커스 토익 RC'를 검색한 뒤 가장 먼저 표시되는 도서의 출간일을 알려줘.\n</user_request>\n" + obs},
	{"type": "image", "image": Image.open("screen.png").convert("RGB")},
	{"type": "text", "text": "\n</observation>"},
	]},
	]
	inputs = processor.apply_chat_template(messages, tools=TOOLS, add_generation_prompt=True,
	tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=16384, do_sample=True, temperature=0.6, top_p=0.95, top_k=20)
	print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))
	```

	A real first step from our Online-Mind2Web run (Aladin home page):

	```text
	<think>I see the Aladin homepage, with the search field labeled “통합검색” near the top. I have not yet searched for the book, so I still need to enter “해커스 토익 RC,” inspect the first result’s publication date, and report that date. My plan:
	1. Click the search field
	2. Enter “해커스 토익 RC” and submit the search
	3. Read the publication date of the first listed book
	This click targets the visible search field and is a safe, relevant first action.
	</think>

	<tool_call>
	<function=click>
	<parameter=x>
	482
	</parameter>
	<parameter=y>
	212
	</parameter>
	</function>
	</tool_call>
	```

	Coordinates are normalized to 0–1000. Convert them to pixels with `x / 1000 × width` and `y / 1000 × height`.

	### Run the agent (vLLM)

	vLLM does not include the KORMo architecture either. The KORMo text layers have the Llama structure, so `vllm/convert_to_vllm.py` renames two parameter names and writes a folder vLLM loads natively. `vllm/sitecustomize.py` turns SigLIP post-layernorm back on inside vLLM.

	1. Convert (once)

	```bash
	pip install -U "vllm>=0.23.0" openai playwright safetensors
	playwright install chromium
	python KORMo-Web-10B/vllm/convert_to_vllm.py KORMo-Web-10B KORMo-Web-10B-vllm
	```

	2. Start the model server

	```bash
	PYTHONPATH=$PWD/KORMo-Web-10B/vllm vllm serve ./KORMo-Web-10B-vllm --served-model-name KORMo-Web-10B \
	--max-model-len 65536 --limit-mm-per-prompt '{"image":1,"video":0}'
	```

	Check that the server log prints `[kormo-vllm] SigLIP post_layernorm enabled`.

	3. Run a browser task

	```bash
	python KORMo-Web-10B/examples/agent_playwright.py --model KORMo-Web-10B \
	--task "다나와에서 50만 원 이하, 10인치 이상 태블릿 3개를 찾아서 가격을 알려줘"
	```

	The agent opens Chromium, prints each action as it goes, and finishes with the model's answer.

	## How It Works

	```
	user <user_request> task </user_request> + observation (tabs, url, screenshot)
	assistant <think> what I see · what is done · plan </think> <tool_call> one action </tool_call>
	tool action result + new observation (newest screenshot only)
	… repeat until finished(answer)
	```

	- Action space: 22 browser tools: click, type, hotkey, scroll, drag, goto, tab control, `finished`, and others ([`tools.json`](tools.json)).
	- Memory: the full text history of thoughts, actions and observations is kept. Only the current screenshot is sent.
	- Recommended settings: temperature 0.6, top_p 0.95, top_k 20, thinking on, up to 16k tokens per step and 50 steps. These are already set in `generation_config.json`.

	The exact message format, parsing rules and feedback strings are in [HARNESS.md](HARNESS.md). Follow it if you build your own harness.

	## Evaluation Details

	\| \| \|
	\|---\|---\|
	\| Tasks \| Online-Mind2Web: 300 English, 312 Korean \|
	\| Judge \| GPT-6 \|

	## Files

	\| File \| Description \|
	\|---\|---\|
	\| `model.safetensors` · `config.json` \| Weights and config (LLaVA-OneVision: SigLIP + KORMo-10B text model) \|
	\| `_configuration_kormo.py` · `_modeling_kormo.py` \| KORMo text model code for transformers 5 \|
	\| `kormo_load.py` \| `register()` and `patch_vision()` \|
	\| `chat_template.jinja` \| Chat template (XML tool calls, thinking on) \|
	\| `system_prompt.md` · `tools.json` \| System prompt and 22-tool schema \|
	\| `HARNESS.md` \| Full harness specification \|
	\| `examples/agent_playwright.py` \| Reference browser agent (Playwright + OpenAI-compatible API). It does not execute `upload`. \|
	\| `vllm/convert_to_vllm.py` · `vllm/sitecustomize.py` \| Convert for vLLM and the post-layernorm fix \|
	\| `generation_config.json` · `processor_config.json` · `tokenizer.json` · `special_tokens_map.json` \| Sampling, processor and tokenizer configs \|

	## 한국어 요약

	KORMo-Web-10B는 KAIST MLP 연구실이 처음부터 학습한 한국어 모델 KORMo-10B → KORMo-VL을 이어 만든 한국어 우선 웹 에이전트입니다. 스크린샷만 보고 한국어·영어 웹 작업을 수행합니다.

	- Online-Mind2Web: EN 29.0, KO 30.1
	- 비교한 공개 웹 에이전트 중 한국어 점수가 가장 높습니다.

	## Acknowledgements

	Built on [KORMo-VL](https://huggingface.co/KORMo-VL/KORMo-VL) and [KORMo-10B](https://huggingface.co/KORMo-Team/KORMo-10B-sft) by KAIST MLP Lab. Evaluated on Online-Mind2Web and its Korean counterpart.

	</div>