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}
}

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

HumanF-MarkrAI/Gukbap-Gemma3-4B-VL🍚

Model Details🍚

Model Description

Model Sources

Implications🍚

Training Method (SFT)🧐

SFT Text-Datasets (Private)

Benchmakrs🤗

Korean MM Benchmark Score (Zero-shot)

MM Benchmarks

Gukbap-VL Series models🍚🍚

BibTeX

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