Instructions to use websfactory/Webs-Sejong-31B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use websfactory/Webs-Sejong-31B-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="websfactory/Webs-Sejong-31B-v2")
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("websfactory/Webs-Sejong-31B-v2")
model = AutoModelForMultimodalLM.from_pretrained("websfactory/Webs-Sejong-31B-v2")
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 websfactory/Webs-Sejong-31B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "websfactory/Webs-Sejong-31B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "websfactory/Webs-Sejong-31B-v2",
		"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/websfactory/Webs-Sejong-31B-v2

SGLang

How to use websfactory/Webs-Sejong-31B-v2 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 "websfactory/Webs-Sejong-31B-v2" \
    --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": "websfactory/Webs-Sejong-31B-v2",
		"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 "websfactory/Webs-Sejong-31B-v2" \
        --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": "websfactory/Webs-Sejong-31B-v2",
		"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 websfactory/Webs-Sejong-31B-v2 with Docker Model Runner:
```
docker model run hf.co/websfactory/Webs-Sejong-31B-v2
```

Webs-Sejong-31B-v2

Webs-Sejong-31B-v2 is a 31B-parameter Korean-centric language model built on the Gemma-4 architecture through weight-space model merging — it is a merge model, not a separately trained one, and we state that openly. It is optimized for Korean cultural knowledge and academic / professional reasoning, with English ability retained from the base.

No additional training is applied.

Highlights

Korean-first. Tuned for Korean cultural knowledge and professional / academic reasoning, with English ability retained from the base.
Drop-in. Standard Gemma-4 architecture and tokenizer; loads in transformers and serves in vLLM with no custom code.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "websfactory/Webs-Sejong-31B-v2"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

Intended Use & Limitations

Intended for Korean-language assistance, knowledge QA, and reasoning. As a merged model it inherits the capabilities and biases of its sources and should be evaluated for your use case before deployment.

License

This model is a derivative of Gemma-4 and is distributed under the Gemma Terms of Use. By using this model you agree to those terms and Google's Prohibited Use Policy.

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Safetensors

Model size

31B params

Tensor type

BF16

Model tree for websfactory/Webs-Sejong-31B-v2

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(195)

this model