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---
language:
- en
- ko
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
model_id: kakaocorp/kanana-nano-2.1b-base
repo: kakaocorp/kanana-nano-2.1b-base
developers: Kanana LLM
training_regime: bf16 mixed precision
model-index:
- name: kanana-nano-2.1b-base
results:
- task:
type: multiple_choice
name: mmlu
dataset:
name: mmlu (5-shots)
type: hails/mmlu_no_train
metrics:
- type: acc
value: 54.83
name: acc
- task:
type: generate_until
name: kmmlu
dataset:
name: kmmlu-direct (5-shots)
type: HAERAE-HUB/KMMLU
metrics:
- type: exact_match
value: 44.83
name: exact_match
- task:
type: multiple_choice
name: haerae
dataset:
name: haerae (5-shots)
type: HAERAE-HUB/HAE_RAE_BENCH
metrics:
- type: acc_norm
value: 77.09
name: acc_norm
- task:
type: generate_until
name: gsm8k
dataset:
name: gsm8k (5-shots)
type: openai/gsm8k
metrics:
- type: exact_match
value: 46.32
name: exact_match_strict
- task:
type: generate_until
name: humaneval
dataset:
name: humaneval (0-shots)
type: openai/openai_humaneval
metrics:
- type: pass@1
value: 31.10
name: pass@1
- task:
type: generate_until
name: mbpp
dataset:
name: mbpp (3-shots)
type: google-research-datasets/mbpp
metrics:
- type: pass@1
value: 46.20
name: pass@1
---
# Kanana
<p align="center">
<br>
<picture>
<img src="./assets/logo/kanana-logo.png" width="60%" style="margin: 40px auto;">
</picture>
</br>
<p align="center"> πŸ€— <a href="https://huggingface.co/collections/kakaocorp/kanana-nano-21b-67a326cda1c449c8d4172259">Models</a> &nbsp | &nbsp πŸ“• <a href="https://tech.kakao.com/posts/689">Blog</a> &nbsp | &nbsp πŸ“œ <a href="https://arxiv.org/abs/2502.18934">Technical Report</a> | &nbsp πŸ’» <a href="https://github.com/kakao/kanana"> Github </a>
<br>
<br>
## Introduction
We introduce Kanana, a series of bilingual language models (developed by [Kakao](https://github.com/kakao)) that demonstrate exceeding performance in Korean and competitive performance in English. The computational cost of Kanana is significantly lower than that of state-of-the-art models of similar size. The report details the techniques employed during pre-training to achieve compute-efficient yet competitive models, including high-quality data filtering, staged pre-training, depth up-scaling, and pruning and distillation. Furthermore, the report outlines the methodologies utilized during the post-training of the Kanana models, encompassing supervised fine-tuning and preference optimization, aimed at enhancing their capability for seamless interaction with users. Lastly, the report elaborates on plausible approaches used for language model adaptation to specific scenarios, such as embedding, function calling, and Retrieval Augmented Generation (RAG). The Kanana model series spans from 2.1B to 32.5B parameters with 2.1B models (base, instruct, embedding, function call, and RAG) publicly released to promote research on Korean language models.
> [!Note]
> Neither the pre-training nor the post-training data includes Kakao user data.
<p align="center">
<picture>
<img src="assets/performance/flops-vs-mmlu.jpg", width="700" style="margin: 40px auto;">
</picture>
<br>
## Table of Contents
- [News](#news)
- [Performance](#performance)
- [Quickstart](#quickstart)
- [License](#license)
- [Citation](#citation)
- [Contributors](#contributors)
- [Contact](#contact)
<br>
## News
- πŸ“œ`2025/02/27`: Released [Technical Report](https://arxiv.org/abs/2502.18934) and πŸ€—[HF model weights](https://huggingface.co/collections/kakaocorp/kanana-nano-21b-67a326cda1c449c8d4172259).
- πŸ“•`2025/01/10`: Published a blog post about the development of `Kanana-Nano` model. ([Kanana-Nano](https://tech.kakao.com/posts/682))
- πŸ“•`2024/11/14`: Published blog posts about the development of `Kanana` models. ([Kanana LLM: Pre-training](https://tech.kakao.com/posts/661), [Kanana LLM: Post-training](https://tech.kakao.com/posts/662))
- ▢️`2024/11/06`: Published a presentation video about the development of the `Kanana` models. ([if(kakaoAI)2024](https://youtu.be/HTBl142x9GI?si=o_we6t9suYK8DfX3))
<br>
## Performance
Below are partial report on the performance of the `Kanana` model series. Please refer to the [Technical Report](https://arxiv.org/abs/2502.18934) for the full results.
### Pre-trained Model Performance
<table>
<tr>
<th>Models</th>
<th>MMLU</th>
<th>KMMLU</th>
<th>HAERAE</th>
<th>HumanEval</th>
<th>MBPP</th>
<th>GSM8K</th>
</tr>
<tr>
<th colspan="8" height="30px">27b+ scale</th>
</tr>
<tr>
<td>Kanana-Flag-32.5b</td>
<td align="center">77.68</td>
<td align="center">62.10</td>
<td align="center"><strong>90.47</strong></td>
<td align="center"><strong>51.22</strong></td>
<td align="center">63.40</td>
<td align="center">70.05</td>
</tr>
<tr>
<td>Qwen2.5-32b</td>
<td align="center"><strong>83.10</strong></td>
<td align="center"><strong>63.15</strong></td>
<td align="center">75.16</td>
<td align="center">50.00</td>
<td align="center">73.40</td>
<td align="center"><strong>82.41</strong></td>
</tr>
<tr>
<td>Gemma-2-27b</td>
<td align="center">75.45</td>
<td align="center">51.16</td>
<td align="center">69.11</td>
<td align="center"><strong>51.22</strong></td>
<td align="center">64.60</td>
<td align="center">74.37</td>
</tr>
<tr>
<td>EXAONE-3.5-32b</td>
<td align="center">72.68</td>
<td align="center">46.36</td>
<td align="center">82.22</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td>Aya-Expanse-32b</td>
<td align="center">74.52</td>
<td align="center">49.57</td>
<td align="center">80.66</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<th colspan="8" height="30px">7b+ scale</th>
</tr>
<tr>
<td>Kanana-Essence-9.8b</td>
<td align="center">67.61</td>
<td align="center">50.57</td>
<td align="center"><strong>84.98</strong></td>
<td align="center">40.24</td>
<td align="center">53.60</td>
<td align="center">63.61</td>
</tr>
<tr>
<td>Llama-3.1-8b</td>
<td align="center">65.18</td>
<td align="center">41.02</td>
<td align="center">61.78</td>
<td align="center">35.37</td>
<td align="center">48.60</td>
<td align="center">50.87</td>
</tr>
<tr>
<td>Qwen2.5-7b</td>
<td align="center"><strong>74.19</strong></td>
<td align="center"><strong>51.68</strong></td>
<td align="center">67.46</td>
<td align="center"><strong>56.71</strong></td>
<td align="center"><strong>63.20</strong></td>
<td align="center"><strong>83.85</strong></td>
</tr>
<tr>
<td>Gemma-2-9b</td>
<td align="center">70.34</td>
<td align="center">48.18</td>
<td align="center">66.18</td>
<td align="center">37.20</td>
<td align="center">53.60</td>
<td align="center">68.16</td>
</tr>
<tr>
<td>EXAONE-3.5-7.8b</td>
<td align="center">65.36</td>
<td align="center">45.30</td>
<td align="center">77.54</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<td>Aya-Expanse-8b</td>
<td align="center">62.52</td>
<td align="center">40.11</td>
<td align="center">71.95</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<th colspan="8" height="30px">2b+ scale</th>
</tr>
<tr>
<td>Kanana-Nano-2.1b</td>
<td align="center">54.83</td>
<td align="center">44.80</td>
<td align="center"><strong>77.09</strong></td>
<td align="center">31.10</td>
<td align="center">46.20</td>
<td align="center">46.32</td>
</tr>
<tr>
<td>Llama-3.2-3b</td>
<td align="center">56.40</td>
<td align="center">35.57</td>
<td align="center">47.66</td>
<td align="center">25.61</td>
<td align="center">39.00</td>
<td align="center">27.37</td>
</tr>
<tr>
<td>Qwen2.5-3b</td>
<td align="center"><strong>65.57</strong></td>
<td align="center"><strong>45.28</strong></td>
<td align="center">61.32</td>
<td align="center"><strong>37.80</strong></td>
<td align="center"><strong>55.60</strong></td>
<td align="center"><strong>69.07</strong></td>
</tr>
<tr>
<td>Gemma-2-2b</td>
<td align="center">52.89</td>
<td align="center">30.67</td>
<td align="center">45.55</td>
<td align="center">20.12</td>
<td align="center">28.20</td>
<td align="center">24.72</td>
</tr>
<tr>
<td>EXAONE-3.5-2.4b</td>
<td align="center">59.27</td>
<td align="center">43.58</td>
<td align="center">69.65</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
<tr>
<th colspan="8" height="30px">70b+ scale</th>
</tr>
<tr>
<td>Llama-3.1-70b</td>
<td align="center">78.93</td>
<td align="center">53.00</td>
<td align="center">76.35</td>
<td align="center">57.32</td>
<td align="center">66.60</td>
<td align="center">81.73</td>
</tr>
<tr>
<td>Qwen2.5-72b</td>
<td align="center">86.12</td>
<td align="center">68.57</td>
<td align="center">80.84</td>
<td align="center">55.49</td>
<td align="center">76.40</td>
<td align="center">92.04</td>
</tr>
</table>
<br>
### Post-trained Model Performance
#### Instruction-following Benchmarks
<table>
<tr>
<th>Models</th>
<th>MT-Bench</th>
<th>LogicKor</th>
<th>KoMT-Bench</th>
<th>WildBench</th>
<th>IFEval</th>
</tr>
<tr>
<th colspan="8" height="30px">27b+ scale</th>
</tr>
<tr>
<td>Kanana-Flag-32.5b</td>
<td align="center">8.356</td>
<td align="center"><strong>9.524</strong></td>
<td align="center"><strong>8.058</strong></td>
<td align="center">54.14</td>
<td align="center"><strong>0.856</strong></td>
</tr>
<tr>
<td>Qwen2.5-32b</td>
<td align="center">8.331</td>
<td align="center">8.988</td>
<td align="center">7.847</td>
<td align="center">51.13</td>
<td align="center">0.822</td>
</tr>
<tr>
<td>Gemma-2-27b</td>
<td align="center">8.088</td>
<td align="center">8.869</td>
<td align="center">7.373</td>
<td align="center">46.46</td>
<td align="center">0.817</td>
</tr>
<tr>
<td>EXAONE-3.5-32b</td>
<td align="center"><strong>8.375</strong></td>
<td align="center">9.202</td>
<td align="center">7.907</td>
<td align="center"><strong>54.30</strong></td>
<td align="center">0.845</td>
</tr>
<tr>
<td>Aya-Expanse-32b</td>
<td align="center">7.788</td>
<td align="center">8.941</td>
<td align="center">7.626</td>
<td align="center">48.36</td>
<td align="center">0.735</td>
</tr>
<tr>
<th colspan="8" height="30px">7b+ scale</th>
</tr>
<tr>
<td>Kanana-Essence-9.8b</td>
<td align="center">7.769</td>
<td align="center">8.964</td>
<td align="center">7.706</td>
<td align="center">47.27</td>
<td align="center">0.799</td>
</tr>
<tr>
<td>Llama-3.1-8b</td>
<td align="center">7.500</td>
<td align="center">6.512</td>
<td align="center">5.336</td>
<td align="center">33.20</td>
<td align="center">0.772</td>
</tr>
<tr>
<td>Qwen2.5-7b</td>
<td align="center">7.625</td>
<td align="center">7.952</td>
<td align="center">6.808</td>
<td align="center">41.31</td>
<td align="center">0.760</td>
</tr>
<tr>
<td>Gemma-2-9b</td>
<td align="center">7.633</td>
<td align="center">8.643</td>
<td align="center">7.029</td>
<td align="center">40.92</td>
<td align="center">0.750</td>
</tr>
<tr>
<td>EXAONE-3.5-7.8b</td>
<td align="center"><strong>8.213</strong></td>
<td align="center"><strong>9.357</strong></td>
<td align="center"><strong>8.013</strong></td>
<td align="center"><strong>50.98</strong></td>
<td align="center"><strong>0.826</strong></td>
</tr>
<tr>
<td>Aya-Expanse-8b</td>
<td align="center">7.131</td>
<td align="center">8.357</td>
<td align="center">7.006</td>
<td align="center">38.50</td>
<td align="center">0.645</td>
</tr>
<tr>
<th colspan="8" height="30px">2b+ scale</th>
</tr>
<tr>
<td>Kanana-Nano-2.1b</td>
<td align="center">6.400</td>
<td align="center">7.964</td>
<td align="center">5.857</td>
<td align="center">25.41</td>
<td align="center">0.720</td>
</tr>
<tr>
<td>Llama-3.2-3b</td>
<td align="center">7.050</td>
<td align="center">4.452</td>
<td align="center">3.967</td>
<td align="center">21.91</td>
<td align="center">0.767</td>
</tr>
<tr>
<td>Qwen2.5-3b</td>
<td align="center">6.969</td>
<td align="center">6.488</td>
<td align="center">5.274</td>
<td align="center">25.76</td>
<td align="center">0.355</td>
</tr>
<tr>
<td>Gemma-2-2b</td>
<td align="center">7.225</td>
<td align="center">5.917</td>
<td align="center">4.835</td>
<td align="center">28.71</td>
<td align="center">0.428</td>
</tr>
<tr>
<td>EXAONE-3.5-2.4b</td>
<td align="center"><strong>7.919</strong></td>
<td align="center"><strong>8.941</strong></td>
<td align="center"><strong>7.223</strong></td>
<td align="center"><strong>41.68</strong></td>
<td align="center"><strong>0.790</strong></td>
</tr>
<tr>
<th colspan="8" height="30px">70b+ scale</th>
</tr>
<tr>
<td>Llama-3.1-70b</td>
<td align="center">8.275</td>
<td align="center">8.250</td>
<td align="center">6.970</td>
<td align="center">46.50</td>
<td align="center">0.875</td>
</tr>
<tr>
<td>Qwen2.5-72b</td>
<td align="center">8.619</td>
<td align="center">9.214</td>
<td align="center">8.281</td>
<td align="center">55.25</td>
<td align="center">0.861</td>
</tr>
</table>
<br>
#### General Benchmarks
<table>
<tr>
<th>Models</th>
<th>MMLU</th>
<th>KMMLU</th>
<th>HAE-RAE</th>
<th>HumanEval+</th>
<th>MBPP+</th>
<th>GSM8K</th>
<th>MATH</th>
</tr>
<tr>
<th colspan="8" height="30px">27b+ scale</th>
</tr>
<tr>
<td>Kanana-Flag-32.5b</td>
<td align="center">81.08</td>
<td align="center"><strong>64.19</strong></td>
<td align="center"><strong>68.18</strong></td>
<td align="center">77.44</td>
<td align="center">69.84</td>
<td align="center">90.83</td>
<td align="center">57.82</td>
</tr>
<tr>
<td>Qwen2.5-32b</td>
<td align="center"><strong>84.40</strong></td>
<td align="center">59.37</td>
<td align="center">48.30</td>
<td align="center"><strong>82.32</strong></td>
<td align="center"><strong>71.96</strong></td>
<td align="center"><strong>95.30</strong></td>
<td align="center"><strong>81.90</strong></td>
</tr>
<tr>
<td>Gemma-2-27b</td>
<td align="center">78.01</td>
<td align="center">49.98</td>
<td align="center">46.02</td>
<td align="center">70.12</td>
<td align="center">70.90</td>
<td align="center">91.05</td>
<td align="center">53.80</td>
</tr>
<tr>
<td>EXAONE-3.5-32b</td>
<td align="center">78.30</td>
<td align="center">55.44</td>
<td align="center">52.27</td>
<td align="center">78.66</td>
<td align="center">70.90</td>
<td align="center">93.56</td>
<td align="center">76.80</td>
</tr>
<tr>
<td>Aya-Expanse-32b</td>
<td align="center">74.49</td>
<td align="center">42.35</td>
<td align="center">51.14</td>
<td align="center">64.63</td>
<td align="center">65.61</td>
<td align="center">75.06</td>
<td align="center">42.82</td>
</tr>
<tr>
<th colspan="8" height="30px">7b+ scale</th>
</tr>
<tr>
<td>Kanana-Essence-9.8b</td>
<td align="center">70.64</td>
<td align="center">50.76</td>
<td align="center"><strong>47.16</strong></td>
<td align="center">72.56</td>
<td align="center">69.05</td>
<td align="center">84.91</td>
<td align="center">42.24</td>
</tr>
<tr>
<td>Llama-3.1-8b</td>
<td align="center">71.18</td>
<td align="center">39.24</td>
<td align="center">40.91</td>
<td align="center">60.98</td>
<td align="center">57.67</td>
<td align="center">82.71</td>
<td align="center">49.86</td>
</tr>
<tr>
<td>Qwen2.5-7b</td>
<td align="center"><strong>77.23</strong></td>
<td align="center">46.87</td>
<td align="center">37.50</td>
<td align="center">73.78</td>
<td align="center"><strong>70.63</strong></td>
<td align="center"><strong>91.58</strong></td>
<td align="center"><strong>75.22</strong></td>
</tr>
<tr>
<td>Gemma-2-9b</td>
<td align="center">73.47</td>
<td align="center">44.47</td>
<td align="center">39.77</td>
<td align="center">59.76</td>
<td align="center">64.55</td>
<td align="center">87.72</td>
<td align="center">48.10</td>
</tr>
<tr>
<td>EXAONE-3.5-7.8b</td>
<td align="center">72.62</td>
<td align="center"><strong>52.09</strong></td>
<td align="center">46.02</td>
<td align="center"><strong>79.27</strong></td>
<td align="center">66.67</td>
<td align="center">89.99</td>
<td align="center">73.50</td>
</tr>
<tr>
<td>Aya-Expanse-8b</td>
<td align="center">61.23</td>
<td align="center">35.78</td>
<td align="center">39.20</td>
<td align="center">42.68</td>
<td align="center">56.88</td>
<td align="center">78.85</td>
<td align="center">30.80</td>
</tr>
<tr>
<th colspan="8" height="30px">2b+ scale</th>
</tr>
<tr>
<td>Kanana-Nano-2.1b</td>
<td align="center">52.48</td>
<td align="center"><strong>38.51</strong></td>
<td align="center"><strong>33.52</strong></td>
<td align="center">63.41</td>
<td align="center">62.43</td>
<td align="center">72.32</td>
<td align="center">29.26</td>
</tr>
<tr>
<td>Llama-3.2-3b</td>
<td align="center">56.09</td>
<td align="center">3.07</td>
<td align="center">17.05</td>
<td align="center">56.71</td>
<td align="center">50.26</td>
<td align="center">66.57</td>
<td align="center">38.18</td>
</tr>
<tr>
<td>Qwen2.5-3b</td>
<td align="center"><strong>69.18</strong></td>
<td align="center">38.33</td>
<td align="center">32.39</td>
<td align="center">67.68</td>
<td align="center"><strong>64.02</strong></td>
<td align="center"><strong>84.00</strong></td>
<td align="center"><strong>65.72</strong></td>
</tr>
<tr>
<td>Gemma-2-2b</td>
<td align="center">57.69</td>
<td align="center">6.99</td>
<td align="center">7.95</td>
<td align="center">35.37</td>
<td align="center">45.24</td>
<td align="center">49.81</td>
<td align="center">21.68</td>
</tr>
<tr>
<td>EXAONE-3.5-2.4b</td>
<td align="center">63.19</td>
<td align="center">14.27</td>
<td align="center">14.20</td>
<td align="center"><strong>70.73</strong></td>
<td align="center">59.79</td>
<td align="center">83.78</td>
<td align="center">64.04</td>
</tr>
<tr>
<th colspan="8" height="30px">70b+ scale</th>
</tr>
<tr>
<td>Llama-3.1-70b</td>
<td align="center">83.48</td>
<td align="center">39.08</td>
<td align="center">53.41</td>
<td align="center">75.61</td>
<td align="center">66.40</td>
<td align="center">91.66</td>
<td align="center">63.98</td>
</tr>
<tr>
<td>Qwen2.5-72b</td>
<td align="center">87.14</td>
<td align="center">65.78</td>
<td align="center">60.80</td>
<td align="center">81.10</td>
<td align="center">75.66</td>
<td align="center">95.45</td>
<td align="center">82.60</td>
</tr>
</table>
<br>
### Embedding Model Performance
<table>
<tr>
<td align="center">Backbone</td>
<td align="center">Kanana-Nano-2.1b</td>
<td align="center">Llama-3.2-3b</td>
<td align="center">Qwen2.5-3b</td>
<td align="center">Llama-3.2-1b</td>
<td align="center">Qwen-2.5-1.5b</td>
</tr>
<tr>
<td align="center">English</td>
<td align="center">51.56</td>
<td align="center">53.28</td>
<td align="center"><strong>54.00</strong></td>
<td align="center">48.77</td>
<td align="center">50.60</td>
</tr>
<tr>
<td align="center">Korean</td>
<td align="center"><strong>65.00</strong></td>
<td align="center">59.43</td>
<td align="center">62.10</td>
<td align="center">54.68</td>
<td align="center">54.60</td>
</tr>
<tr>
<td align="center">Avg.</td>
<td align="center"><strong>58.28</strong></td>
<td align="center">56.35</td>
<td align="center">58.05</td>
<td align="center">51.73</td>
<td align="center">52.60</td>
</tr>
</table>
<br>
## Quickstart
### πŸ€— HuggingFace Transformers
- `transformers>=4.45.0` or the latest version is required to run `Kanana` model.
```bash
pip install transformers>=4.45.0
```
#### Example Usage for `kanana-nano-2.1b-base`
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "kakaocorp/kanana-nano-2.1b-base"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
prompt1 = "이처럼 μΈκ°„μ²˜λŸΌ μƒκ°ν•˜κ³  ν–‰λ™ν•˜λŠ” AI λͺ¨λΈμ€ "
prompt2 = "Kakao is a leading company in South Korea, and it is known for "
input_ids = tokenizer(
[prompt1, prompt2],
padding=True,
return_tensors="pt",
)["input_ids"].to("cuda")
_ = model.eval()
with torch.no_grad():
output = model.generate(
input_ids,
max_new_tokens=32,
do_sample=False,
)
decoded = tokenizer.batch_decode(output, skip_special_tokens=True)
for text in decoded:
print(text)
# Output:
# 이처럼 μΈκ°„μ²˜λŸΌ μƒκ°ν•˜κ³  ν–‰λ™ν•˜λŠ” AI λͺ¨λΈμ€ 2020λ…„λŒ€ μ€‘λ°˜μ— λ“±μž₯ν•  κ²ƒμœΌλ‘œ μ˜ˆμƒλœλ‹€. 2020λ…„λŒ€ μ€‘λ°˜μ— λ“±μž₯ν•  κ²ƒμœΌλ‘œ μ˜ˆμƒλ˜λŠ” AI λͺ¨λΈμ€ 인간
# Kakao is a leading company in South Korea, and it is known for 1) its innovative products and services, 2) its commitment to sustainability, and 3) its focus on customer experience. Kakao has been recognized as
```
<br>
## License
The `Kanana` models are licensed under [CC-BY-NC-4.0](https://spdx.org/licenses/CC-BY-NC-4.0).
<br>
## Citation
```
@misc{kananallmteam2025kananacomputeefficientbilinguallanguage,
title={Kanana: Compute-efficient Bilingual Language Models},
author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo},
year={2025},
eprint={2502.18934},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.18934},
}
```
<br>
## Contributors
- Pre-training: Yunju Bak, Doohae Jung, Boseop Kim, Nayeon Kim, Hojin Lee, Jaesun Park, Minho Ryu
- Post-training: Jiyeon Ham, Seungjae Jung, Hyunho Kim, Hyunwoong Ko, Changmin Lee, Daniel Wontae Nam, Kyoung-Woon On
- Adaptation: Seulye Baeg, Junrae Cho, Taegyeong Eo, Sunghee Jung, Jieun Kang, EungGyun Kim, Eunhwa Kim, Byeongil Ko, Daniel Lee, Donghun Lee, Minchul Lee, Miok Lee, Shinbok Lee, Minho Ryu, Gaeun Seo
<br>
## Contact
- Kanana LLM Team Technical Support: [email protected]
- Business & Partnership Contact: [email protected]