Instructions to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF",
	filename="Llama-DNA-1.0-8B-Instruct-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

Use Docker

docker model run hf.co/dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

SGLang

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF 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 "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF" \
    --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": "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF" \
        --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": "dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
```

Unsloth Studio

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16
```

Lemonade

How to use dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.Llama-DNA-1.0-8B-Instruct-GGUF-F16

List all available models

lemonade list

DNA 1.0 8B Instruct GGUF

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.

# 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.

$ 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}, 
}