DNA 1.0 8B Instruct is a state-of-the-art (SOTA) bilingual language model based on Llama architecture, specifically optimized for Korean language understanding and generation, while also maintaining strong English capabilities. The model was developed through a sophisticated process involving model merging via spherical linear interpolation (SLERP) with Llama 3.1 8B Instruct, and underwent knowledge distillation (KD) using Llama 3.1 405B as the teacher model. It was extensively trained through continual pre-training (CPT) with a high-quality Korean dataset. The training pipeline was completed with supervised fine-tuning (SFT) and direct preference optimization (DPO) to align with human preferences and enhance instruction-following abilities.

Quickstart

We offer weights in F32, F16 formats and quantized weights in Q8_0, Q6_K, Q5_K, Q4_K, Q3_K and Q2_K formats.

You can run GGUF weights with llama.cpp as follows:

Install llama.cpp. Please refer to the llama.cpp repository for more details.
Download DNA 1.0 8B Instruct model in GGUF format.

bash

# Install huggingface_hub if not already installed
$ pip install huggingface_hub[cli]

# Download the GGUF weights
$ huggingface-cli download dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF \
    --include "Llama-DNA-1.0-8B-Instruct-Q8_0.gguf" \
    --local-dir .

Run the model with llama.cpp in conversational mode.

bash

$ llama-cli -cnv -m ./Llama-DNA-1.0-8B-Instruct-Q8_0.gguf \
    -p "You are a helpful assistant, Dnotitia DNA."

Ollama

DNA 1.0 8B Instruct model is compatible with Ollama. You can use it as follows:

Install Ollama. Please refer to the Ollama repository for more details.
Run the model with Ollama.

$ ollama run dnotitia/dna

Limitations

While DNA 1.0 8B Instruct demonstrates strong performance, users should be aware of the following limitations:

The model may occasionally generate biased or inappropriate content.
Responses are based on training data and may not reflect current information.
The model may sometimes produce factually incorrect or inconsistent answers.
Performance may vary depending on the complexity and domain of the task.
Generated content should be reviewed for accuracy and appropriateness.

License

The model is released under the CC BY-NC 4.0 license. For commercial usage inquiries, please Contact us.

Citation

If you use or discuss this model in your academic research, please cite the project to help spread awareness:

@misc{lee2025dna10technicalreport,
      title={DNA 1.0 Technical Report}, 
      author={Jungyup Lee and Jemin Kim and Sang Park and SeungJae Lee},
      year={2025},
      eprint={2501.10648},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.10648}, 
}