Instructions to use Markr-AI/Gukbap-Gemma3-4B-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Markr-AI/Gukbap-Gemma3-4B-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="Markr-AI/Gukbap-Gemma3-4B-VL")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Markr-AI/Gukbap-Gemma3-4B-VL")
model = AutoModelForImageTextToText.from_pretrained("Markr-AI/Gukbap-Gemma3-4B-VL")
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

vLLM

How to use Markr-AI/Gukbap-Gemma3-4B-VL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Markr-AI/Gukbap-Gemma3-4B-VL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Markr-AI/Gukbap-Gemma3-4B-VL",
		"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/Markr-AI/Gukbap-Gemma3-4B-VL

SGLang

How to use Markr-AI/Gukbap-Gemma3-4B-VL 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 "Markr-AI/Gukbap-Gemma3-4B-VL" \
    --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": "Markr-AI/Gukbap-Gemma3-4B-VL",
		"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 "Markr-AI/Gukbap-Gemma3-4B-VL" \
        --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": "Markr-AI/Gukbap-Gemma3-4B-VL",
		"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 Markr-AI/Gukbap-Gemma3-4B-VL with Docker Model Runner:
```
docker model run hf.co/Markr-AI/Gukbap-Gemma3-4B-VL
```

HumanF-MarkrAI/Gukbap-Gemma3-4B-VL🍚

Model Details🍚

Model Description

Developed by: HumanF-MarkrAI
Model type: Korean-VL-Gemma3-4B
Language(s): Korean + English
Context Length: 2048
License: cc-by-4.0
Finetuned from model: google/gemma-3-4b-it.

Model Sources

When training, we used H100 80GB GPUx4.

Implications🍚

If you want to know our model's details, please see 🔥Gukbap-LMM Blog🔥.

Training Method (SFT)🧐

The following papers contain the foundational methodologies for the dataset and training methods we are currently proceeding.

LIMA.
Gemma3.

SFT Text-Datasets (Private)

When we made the Open-Source based dataset, we use microsoft/WizardLM-2-8x22B through DeepInfra.
Our datasets are made by Evolving system, which is propsed by WizardLM. In training, we used 1849 training dataset, and 200 validation dataset.

Wizard-Korea-Datasets: MarkrAI/Markr_WizardLM_train_ver4.

Learning rate: 1e-5; Epoch: 5

Benchmakrs🤗

Korean MM Benchmark Score (Zero-shot)

We internally evaluated 🔥our code🔥.
We utilized gpt-4o-2024-08-06 in K-LLAVA-W evaluation.

Model	K-MMBench	K-MMStar	K-DTCBench	K-LLAVA-W	AVG
Gukbap-Gemma3-4B🍚	74.73	40.67	44.17	60.00	54.89
gemma-3-4b-it	75.84	40.60	49.58	62.67	57.17
Gukbap-Gemma2-9B-VL🍚	80.16	54.20	52.92	63.83	62.78
Ovis1.6-Gemma2-9B	52.46	50.40	47.08	55.67	51.40
VARCO-VISION-14B	87.16	58.13	85.42	51.17	70.47
llama-3.2-Korean-Bllossom-AICA-5B	26.01	21.60	17.08	45.33	27.51

MM Benchmarks

Global MM Bench dataset: OpenCampass MM leaderboard
Korean MM Bench dataset: NCSOFT.

Gukbap-VL Series models🍚🍚

BibTeX

@article{HumanF-MarkrAI,
  title={Gukbap-Gemma3-4B-VL},
  author={MarkrAI},
  year={2025},
  url={https://huggingface.co/HumanF-MarkrAI}
}

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

F16

Collection including Markr-AI/Gukbap-Gemma3-4B-VL

Gukbap-Series LMM

Collection

6 items • Updated Apr 2, 2025 • 1

Papers for Markr-AI/Gukbap-Gemma3-4B-VL

Gemma 3 Technical Report

Paper • 2503.19786 • Published Mar 25, 2025 • 56

LIMA: Less Is More for Alignment

Paper • 2305.11206 • Published May 18, 2023 • 27