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KISTI-KONI/KONI-4B-instruct-20250901
KONI-4B-instruct-20250901 is a text generation model from KISTI-KONI. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as gemma.
KONI (KISTI Open Neural Intelligence) is a large language model developed by the Korea Institute of Science and Technology Information (KISTI). Designed specifically for the scientific and technological domains, KONI…
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From the Hugging Face model README
KONI (KISTI Open Neural Intelligence) is a large language model developed by the Korea Institute of Science and Technology Information (KISTI). Designed specifically for the scientific and technological domains, KONI excels in both Korean and English, making it an ideal tool for tasks requiring specialized knowledge in these areas.
KONI-4B-instruct has demonstrated strong performance on a variety of scientific benchmarks, outperforming several other 4B-sized pretrained models. Here is a comparison of KONI-4B-instruct’s performance across various benchmarks including scientific and technological benchmarks:
| Rank | Model | KMMLU | KMMLU-Hard | KMMLU-Direct | KoBEST | HAERAE | kormedmcqa | MMLU | ARC_easy | ARC_challenge | Hellaswag | ScholarBench-MC | AidaBench-MC | average |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Qwen/Qwen3-8B | 0.5500 | 0.2900 | 0.5558 | 0.7800 | 0.6700 | 0.3750 | 0.7400 | 0.8700 | 0.6400 | 0.5700 | 0.7094 | 0.7314 | 0.623462 |
| 2 | kakaocorp/kanana-1.5-8b-base | 0.4800 | 0.2500 | 0.4872 | 0.6200 | 0.8200 | 0.5910 | 0.6300 | 0.8300 | 0.5600 | 0.6000 | 0.6800 | 0.7548 | 0.608580 |
| 3 | LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct | 0.4700 | 0.2300 | 0.4532 | 0.5900 | 0.7800 | 0.5310 | 0.6500 | 0.8300 | 0.5900 | 0.6200 | 0.6900 | 0.7057 | 0.594986 |
| 4 | KISTI-KONI/KONI-4B-instruct-20250901 | 0.4188 | 0.2110 | 0.4194 | 0.7393 | 0.7333 | 0.4719 | 0.5823 | 0.8342 | 0.5452 | 0.5783 | 0.6980 | 0.6274 | 0.571603 |
| 5 | kakaocorp/kanana-1.5-2.1b-instruct-2505 | 0.4200 | 0.2100 | 0.4247 | 0.7700 | 0.7900 | 0.5224 | 0.5500 | 0.8000 | 0.5300 | 0.5100 | 0.6630 | 0.6688 | 0.571577 |
| 6 | KISTI-KONI/KONI-4B-base-20250819 | 0.4300 | 0.2100 | 0.4349 | 0.7300 | 0.6600 | 0.4800 | 0.5800 | 0.8200 | 0.5200 | 0.5700 | 0.6800 | 0.6147 | 0.560803 |
| 7 | LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct | 0.4300 | 0.2100 | 0.4379 | 0.7400 | 0.6600 | 0.4842 | 0.5900 | 0.7700 | 0.5000 | 0.5400 | 0.6900 | 0.6511 | 0.558603 |
| 8 | KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024 | 0.4000 | 0.2000 | 0.4100 | 0.5600 | 0.6400 | 0.4905 | 0.6300 | 0.8300 | 0.5400 | 0.6100 | 0.6980 | 0.6722 | 0.556725 |
| 9 | meta-llama/Llama-3.1-8B-Instruct | 0.4000 | 0.2000 | 0.4119 | 0.7000 | 0.4400 | 0.4789 | 0.6500 | 0.8400 | 0.5400 | 0.6100 | 0.6960 | 0.6709 | 0.553135 |
| 10 | google/gemma-3-4b-pt | 0.3980 | 0.1998 | 0.3966 | 0.6990 | 0.6672 | 0.4726 | 0.5964 | 0.8300 | 0.5435 | 0.5763 | 0.6670 | 0.5886 | 0.552906 |
| 11 | google/gemma-3-4b-it | 0.3900 | 0.2100 | 0.3904 | 0.7200 | 0.5900 | 0.4400 | 0.5800 | 0.8400 | 0.5600 | 0.5600 | 0.6990 | 0.6013 | 0.548388 |
| 12 | saltlux/Ko-Llama3-Luxia-8B | 0.3800 | 0.2100 | 0.3935 | 0.7100 | 0.6800 | 0.4320 | 0.5500 | 0.8000 | 0.4800 | 0.5600 | 0.6650 | 0.6109 | 0.539283 |
| 13 | MLP-KTLim/llama-3-Korean-Bllossom-8B | 0.3700 | 0.2200 | 0.3738 | 0.5500 | 0.4700 | 0.4163 | 0.6400 | 0.8400 | 0.5700 | 0.5900 | 0.6525 | 0.5862 | 0.523239 |
| 14 | kakaocorp/kanana-1.5-2.1b-base | 0.3900 | 0.2400 | 0.4502 | 0.6200 | 0.5700 | 0.5138 | 0.4700 | 0.7300 | 0.4400 | 0.4500 | 0.6500 | 0.6478 | 0.514315 |
| 15 | naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B | 0.3900 | 0.2400 | 0.3524 | 0.6400 | 0.5700 | 0.3550 | 0.4700 | 0.7300 | 0.4400 | 0.4500 | 0.5950 | 0.5450 | 0.481447 |
| 16 | naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B | 0.3700 | 0.2200 | 0.3798 | 0.6200 | 0.5600 | 0.3383 | 0.4400 | 0.7200 | 0.3900 | 0.4100 | 0.5600 | 0.5173 | 0.460449 |
| 17 | mistralai/Mistral-7B-v0.3 | 0.3700 | 0.2200 | 0.3739 | 0.6300 | 0.3700 | 0.3735 | 0.6200 | 0.8300 | 0.5500 | 0.6200 | 0.5440 | 0.4257 | 0.413117 |
| 18 | google/gemma-3-1b-it | 0.3069 | 0.2400 | 0.2935 | 0.3556 | 0.5987 | 0.2761 | 0.3970 | 0.6620 | 0.3430 | 0.4204 | 0.5720 | 0.3972 | 0.390038 |
| 19 | google/gemma-3-1b-pt | 0.2582 | 0.2456 | 0.2556 | 0.5569 | 0.1952 | 0.1964 | 0.2641 | 0.7146 | 0.3541 | 0.4703 | 0.2192 | 0.1980 | 0.327362 |
| 20 | etri-lirs/eagle-3b-preview | 0.1600 | 0.2100 | 0.1617 | 0.5100 | 0.1900 | 0.1804 | 0.2500 | 0.5700 | 0.2400 | 0.3700 | 0.2678 | 0.2224 | 0.236846 |

As shown, KISTI-KONI/KONI-4B-instruct-20250901 is the top-performing model in the 4B-size instruction-tuned model category, outperforming google/gemma-3-4b-it and KISTI-KONI/KONI-4B-base-20250819.
$ pip install -U transformers
from transformers import pipeline
import torch
pipe = pipeline("text-generation", model="KISTI-KONI/KONI-4B-instruct-20250901", device="cuda", torch_dtype=torch.bfloat16)
messages = [
[
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."},]
},
{
"role": "user",
"content": [{"type": "text", "text": "슈퍼컴퓨터에 대해서 설명해줘."},]
},
],
]
output = pipe(
messages,
max_new_tokens=512,
eos_token_id=[pipe.tokenizer.eos_token_id, pipe.tokenizer.convert_tokens_to_ids("<end_of_turn>")]
)
If you use this model in your work, please cite it as follows:
@article{KISTI-KONI/KONI-4B-instruct-20250901,
title={KISTI-KONI/KONI-4B-instruct-20250901},
author={KISTI},
year={2025},
url={https://huggingface.co/KISTI-KONI/KONI-4B-instruct-20250901}
}