kanana-nano-2.1b-instruct

Kanana

<p align="center"> <br> <picture> <img src="./assets/logo/kanana-logo.png" width="60%" style="margin: 40px auto;"> </picture> </br> <p align="center"> 🤗 <a href="https://huggingface.co/collections/kakaocorp/kanana-nano-21b-67a326cda1c449c8d4172259">Models</a> &nbsp | &nbsp 📕 <a href="https://tech.kakao.com/posts/689">Blog</a> &nbsp | &nbsp 📜 <a href="https://arxiv.org/abs/2502.18934">Technical Report</a> | &nbsp 💻 <a href="https://github.com/kakao/kanana"> Github </a> <br> <br>

Introduction

We introduce Kanana, a series of bilingual language models (developed by 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
• Performance
• Quickstart
• License
• Citation
• Contributors
• Contact

<br>

News

• 📜2025/02/27: Released Technical Report and 🤗HF model weights.
• 📕2025/01/10: Published a blog post about the development of Kanana-Nano model. (Kanana-Nano)
• 📕2024/11/14: Published blog posts about the development of Kanana models. (Kanana LLM: Pre-training, Kanana LLM: Post-training)
• ▶️2024/11/06: Published a presentation video about the development of the Kanana models. (if(kakaoAI)2024)

<br>

Performance

Below are partial report on the performance of the Kanana model series. Please refer to the Technical Report 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

🤗 HuggingFace Transformers

• transformers>=4.45.0 or the latest version is required to run Kanana model.

pip install transformers>=4.45.0

Example Usage for kanana-nano-2.1b-instruct

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "kakaocorp/kanana-nano-2.1b-instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Convert these dates to YYYY/MM/DD format:\n12/31/2021\n02-01-22"
messages = [
    {"role": "system", "content": "You are a helpful AI assistant developed by Kakao."},
    {"role": "user", "content": prompt}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

_ = model.eval()
with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=72,
        do_sample=False,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

# Output:
# Sure! Here are the given dates converted to the `YYYY/MM/DD` format:

# 1. **12/31/2021**
#    - **YYYY/MM/DD:** 2021/12/31

# 2. **02-01-22**
#    - **YYYY/MM/DD:** 2022/02/01

# So, the converted dates are ...

<br>

License

The Kanana models are licensed under 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]