Konan-LLM-OND

Overview

Konan-LLM-OND, a large language model from Konan Technology Inc., is based on Qwen3-4B-Base. It has been specifically optimized for the Korean language through vocabulary expansion, continual pre-training, and instruction tuning to enhance performance and efficiency.

• Languages: Primarily Korean, with support for English.
• Key Features:
  • Expanded Korean Vocabulary: The model's vocabulary has been expanded with additional Korean tokens to improve tokenization efficiency. As a result, Konan-LLM-OND is approximately 30% more token-efficient with Korean input than Qwen3, leading to greater cost-effectiveness and processing speed.
  • Continual Pre-training: The model underwent continual pre-training on a large-scale Korean corpus using an expanded vocabulary. This process enhanced its fundamental understanding and text generation capabilities in Korean.
  • Supervised Fine-Tuning (SFT): The model was fine-tuned on a high-quality Korean instruction dataset to improve its ability to understand and execute a wide variety of real-world tasks.

Benchmark Results

Model Performance (＜ 5B)

Model Performance (≥ 7B)

Note:

• The highest scores are shown in bold.

Benchmark Setup

All benchmarks were executed using the following standardized environment.

• Evaluation Framework: lm-evaluation-harness v0.4.9
• Runtime & Hardware: All models were served with vLLM v0.9.2 on NVIDIA GPU.
• Inference Mode: For every benchmark, we invoked the chat_completions API, and scores were computed solely from the generated responses.

Metric Adjustments

• KMMLU was evaluated in a zero-shot setting using a CoT-style prompt modified from the kmmlu_direct task in lm-evaluation-harness, with enhanced preprocessing filters applied during evaluation.

• MMLU was evaluated in a zero-shot setting using a CoT-style prompt modified from the mmlu_generative task in lm-evaluation-harness, with enhanced preprocessing filters applied during evaluation.

• GSM8K was evaluated in a zero-shot setting using the original prompt format from lm-evaluation-harness, with enhanced preprocessing filters applied.

• HRM8K was evaluated in a zero-shot setting using the original prompt and data format from lm-evaluation-harness, without any modifications.

• Ko-IFEval was evaluated in a zero-shot setting using the original IFEval protocol, with the dataset sourced from allganize/IFEval-Ko.

Evaluation Protocol

Quickstart

Konan-LLM-OND is supported in transformers v4.52.0 and later.

pip install transformers>=4.52.0

The code example below shows you how to get the model to generate content based on given inputs.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "konantech/Konan-LLM-OND"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "대한민국 수도는?"}
]


input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=64,
        do_sample=False,
    )

len_input_prompt = len(input_ids[0])
response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True)
print(response)
# 대한민국 수도는 서울입니다.

Citation

@misc{Konan-LLM-OND-2025,
  author = {Konan Technology Inc.},
  title = {Konan-LLM-OND},
  year = {2025},
  url = {https://huggingface.co/konantech/Konan-LLM-OND}
}