README.md

---
language:
- en
- ko
license: cc-by-nc-4.0
tags:
- dnotitia
- nlp
- llm
- slm
- conversation
- chat
base_model:
- dnotitia/Llama-DNA-1.0-8B-Instruct
library_name: transformers
pipeline_tag: text-generation
---

# DNA 1.0 8B Instruct
<br>
<p align="center">
<img src="assets/dna-logo.png" width="400" style="margin: 40px auto;">
</p>
<br>

## Introduction

We introduce **DNA 1.0 8B Instruct**, a state-of-the-art (**SOTA**) bilingual language model optimized for both Korean and English languages, developed and released by **Dnotitia Inc.** This model is based on the Llama architecture and has been meticulously enhanced through various advanced training techniques to excel in language understanding and generation tasks.

The DNA 1.0 8B Instruct model has undergone a sophisticated development process:

- **Model Merging via SLERP:** Combined with Llama 3.1 8B Instruct using spherical linear interpolation to enhance performance.
- **Knowledge Distillation (KD):** Utilizing Llama 3.1 405B as the teacher model to improve knowledge representation.
- **Continual Pre-Training (CPT):** Trained on a high-quality Korean dataset to boost language capabilities.
- **Supervised Fine-Tuning (SFT):** Aligned with human preferences through fine-tuning on curated data.
- **Direct Preference Optimization (DPO):** Enhanced instruction-following abilities for better user interaction.

Each model supports long-context processing of up to **131,072 tokens (128K)**, enabling it to handle extensive conversational histories and long documents effectively.

<br>

## Evaluation

We evaluated DNA 1.0 8B Instruct against other prominent language models of similar sizes across various benchmarks, including Korean-specific tasks and general language understanding metrics.

<br>

<table>
  <tr>
    <th>Language</th>
    <th>Benchmark</th>
    <th>dnotitia<br>DNA 1.0<br>8B Instruct</th>
    <th>EXAONE 3.5<br>7.8B</th>
    <th>Qwen 2.5<br>7B</th>
    <th>Llama 3.1<br>8B</th>
    <th>Mistral<br>7B</th>
  </tr>
  <tr>
    <td rowspan="5">Korean</td>
    <td>KMMLU</td>
    <td align="center"><strong>53.26</strong></td>
    <td align="center">45.30</td>
    <td align="center">45.66</td>
    <td align="center">41.66</td>
    <td align="center">31.45</td>
  </tr>
  <tr>
    <td>KMMLU-Hard</td>
    <td align="center"><strong>29.46</strong></td>
    <td align="center">23.17</td>
    <td align="center">24.78</td>
    <td align="center">20.49</td>
    <td align="center">17.86</td>
  </tr>
  <tr>
    <td>KoBEST</td>
    <td align="center"><strong>83.40</strong></td>
    <td align="center">79.05</td>
    <td align="center">78.51</td>
    <td align="center">67.56</td>
    <td align="center">63.77</td>
  </tr>
  <tr>
    <td>Belebele</td>
    <td align="center"><strong>57.99</strong></td>
    <td align="center">40.97</td>
    <td align="center">54.85</td>
    <td align="center">54.70</td>
    <td align="center">40.31</td>
  </tr>
  <tr>
    <td>CSAT QA</td>
    <td align="center">43.32</td>
    <td align="center">40.11</td>
    <td align="center"><strong>45.45</strong></td>
    <td align="center">36.90</td>
    <td align="center">27.27</td>
  </tr>
  <tr>
    <td rowspan="3">English</td>
    <td>MMLU</td>
    <td align="center">66.64</td>
    <td align="center">65.27</td>
    <td align="center"><strong>74.26</strong></td>
    <td align="center">68.26</td>
    <td align="center">62.04</td>
  </tr>
  <tr>
    <td>MMLU Pro</td>
    <td align="center"><strong>43.05</strong></td>
    <td align="center">40.73</td>
    <td align="center">42.50</td>
    <td align="center">40.92</td>
    <td align="center">33.49</td>
  </tr>
  <tr>
    <td>GSM8K</td>
    <td align="center"><strong>80.52</strong></td>
    <td align="center">65.96</td>
    <td align="center">75.74</td>
    <td align="center">75.82</td>
    <td align="center">49.66</td>
  </tr>
</table>

- The **highest scores** are in **bold**.

<br>

**Evaluation Protocol**

For easy reproduction of our evaluation results, we list the evaluation tools and settings used below:

| Benchmark   | Evaluation Setting | Metric                              | Evaluation Tool    |
|-------------|--------------------|-------------------------------------|--------------------|
| KMMLU       | 5-shot             | `macro_avg` / `exact_match`         | `lm-eval-harness`  |
| KMMLU-Hard  | 5-shot             | `macro_avg` / `exact_match`         | `lm-eval-harness`  |
| KoBEST      | 5-shot             | `macro_avg` / `f1`                  | `lm-eval-harness`  |
| Belebele    | 0-shot             | `accuracy`                          | `lm-eval-harness`  |
| CSAT QA     | 0-shot             | `accuracy_normalized`               | `lm-eval-harness`  |
| MMLU        | 5-shot             | `macro_avg` / `accuracy`            | `lm-eval-harness`  |
| MMLU Pro    | 5-shot             | `macro_avg` / `exact_match`         | `lm-eval-harness`  |
| GSM8K       | 5-shot             | `accuracy` / `exact_match`          | `lm-eval-harness`  |

<br>

## 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 download the GGUF weights as follows:

```bash
# Install huggingface_hub if not already installed
pip install huggingface_hub

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

<br>

## Run Locally

For end users, we introduce two ways to run DNA 1.0 8B Instruct model locally.

> **Note**
>
> We recommend using a repetition penalty not exceeding 1.0 for better generation quality.

### llama.cpp

You can run DNA 1.0 8B Instruct model with `llama.cpp` as follows:

1. Install `llama.cpp`. Please refer to the [llama.cpp repository](https://github.com/ggerganov/llama.cpp) for more details.

2. Download DNA 1.0 8B Instruct model in GGUF format.

```bash
huggingface-cli download dnotitia/Llama-DNA-1.0-8B-Instruct-GGUF \
    --include "DNA-1.0-8B-Instruct-BF16*.gguf" \
    --local-dir .
```

3. Run the model with `llama.cpp` in conversational mode.

```bash
llama-cli -cnv -m ./DNA-1.0-8B-Instruct-BF16.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:

1. Install Ollama. Please refer to the [Ollama repository](https://github.com/ollama/ollama) for more details.

2. Create a `Modelfile` for DNA 1.0 8B Instruct.

```text
# Model path (choose appropriate GGUF weights)
FROM ./DNA-1.0-8B-Instruct-BF16.gguf

# Parameter values
PARAMETER stop "<|endoftext|>"
PARAMETER repeat_penalty 1.0
# PARAMETER num_ctx 131072  # if you need a long context

# Chat template
TEMPLATE """{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{ if eq .Role "system" }}[|system|]{{ .Content }}[|endoftext|]
{{ continue }}
{{ else if eq .Role "user" }}[|user|]{{ .Content }}
{{ else if eq .Role "assistant" }}[|assistant|]{{ .Content }}[|endoftext|]
{{ end }}
{{- if and (ne .Role "assistant") $last }}[|assistant|]{{ end }}
{{- end -}}"""

# System prompt
SYSTEM """You are a helpful assistant, Dnotitia DNA."""

# License
LICENSE """CC BY-NC 4.0"""
```

3. Convert the model to Ollama.

```bash
ollama create dna -f Modelfile
```

4. Run the model with Ollama.

```bash
ollama run dna
```

<br>

## 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.

<br>

## License

The model is released under the [CC BY-NC 4.0 license](./LICENSE). For commercial usage inquiries, please [Contact us](https://www.dnotitia.com/contact/post-form).

<br>

## Citation

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

```
@article{dnotitiadna2024,
  title = {Dnotitia DNA 1.0 8B Instruct},
  author = {Jungyup Lee, Jemin Kim, Sang Park, Seungjae Lee},
  year = {2024},
  url = {https://huggingface.co./dnotitia/DNA-1.0-8B-Instruct},
  version = {1.0},
}
```

<br>

## Contact

For technical support and inquiries: [Contact us](https://www.dnotitia.com/contact/post-form)