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Model: kakaocorp/kanana-2-30b-a3b-instruct-2601 Source: Original Platform
2026-04-14 16:17:00 +08:00
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+KANANA LICENSE AGREEMENT
+
+Kanana Release Date: July 17, 2025
+
+This KANANA LICENSE AGREEMENT (this “Agreement”) is made by and between you and Kakao Corp. (“KAKAO”) that governs your use of Kanana Materials that KAKAO provides to you.
+By using, copying, modifying, distributing, performing, or displaying all or part of Kanana Materials, or otherwise accepting the terms and conditions of this Agreement, you agree to be bound by this Agreement. You hereby represent and warrant that (i) you are legally authorized to enter into this Agreement, and (ii) if you are entering into this Agreement on behalf of a legal entity, you have the authority to legally and validly bind such entity.
+
+1. Definition
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+
+2. Grant of License and Use Policy
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+    2.3 This Agreement applies solely to Kanana-*** and shall not apply to any other models distributed by KAKAO under separate licenses. Licenses applicable to such other models shall not apply to Kanana-***.
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+3. Redistribution
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+
+4. Additional Commercial Terms
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+
+5. Outputs
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+
+6. Disclaimer of Warranty
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+
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+You shall indemnify and hold KAKAO harmless from and against any and all claims that may be filed by a third party as a result of your infringement of any third party’s rights or violation of any applicable law, to the extent caused by your use or distribution of Kanana Materials, Derivative Works, or Outputs; provided, however, that the foregoing shall not apply to claims resulting from KAKAO’s willful or gross negligence.
+
+9. Intellectual Property
+    9.1 This Agreement does not grant you any rights to use KAKAO’s trademarks, service marks, or product names. However, on a limited basis and solely for the purpose of complying with Section 3.1(v), KAKAO authorizes you to use the Kanana trademark, provided that KAKAO may require you to discontinue such use at any time if you impair the value of the Kanana trademark.
+    9.2 KAKAO retains ownership of Kanana Materials and Derivative Works created by KAKAO, but you will retain ownership of any Derivative Works and modifications made by you.
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+    9.4 You acknowledge that Model Weights are a valuable asset of KAKAO. You shall not extract, copy, distribute, modify Model Weights or use them to train new models, except as expressly permitted under this Agreement.
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+
+10. Term and Termination
+The term of this Agreement will commence upon your acceptance of this Agreement or access to Kanana Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. KAKAO may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of Kanana Materials and Derivative Works. Sections 5, 6, 7, 8, 10 and 11 shall survive the termination of this Agreement.
+
+11. Governing Law and Arbitration
+    11.1 This Agreement will be governed and construed under the laws of the Republic of Korea, without regard to its conflicts of laws principles.
+    11.2 Any disputes arising out of or in connection with this Agreement shall be finally settled by arbitration in accordance with the International Arbitration Rules of the Korean Commercial Arbitration Board. The number of arbitrators shall be one. The seat, or legal place, of arbitral proceedings shall be Seoul, Republic of Korea. The language to be used in the arbitral proceedings shall be English. Either party may seek interim or provisional relief from a court of competent jurisdiction, which shall not be considered a waiver of any provision in this Section. The arbitral tribunal also has the authority to issue orders for interim or provisional relief.
+
+12. No Waiver
+KAKAO’s failure or delay in exercising any of its rights under this Agreement shall not constitute a waiver of such rights.
--- a/README.md
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+---
+library_name: transformers
+license_name: "kanana"
+license_link: LICENSE
+pipeline_tag: text-generation
+model_id: kakaocorp/kanana-2-30b-a3b-instruct-2601
+repo: kakaocorp/kanana-2-30b-a3b-instruct-2601
+developers: Kanana LLM
+base_model:
+- kakaocorp/kanana-2-30b-a3b-mid-2601
+---
+
+<p align="center">
+    <img src="./assets/logo/kanana.png" width="60%" alt="Kanana">
+</p>
+
+<p align="center">
+    🤗 <a href="https://huggingface.co/collections/kakaocorp/kanana-2">HF Models</a> &nbsp | &nbsp
+    📕 <a href="https://tech.kakao.com/posts/807">Pre-Training Blog</a> &nbsp | &nbsp
+    📕 <a href="https://tech.kakao.com/posts/808">Post-Training Blog</a> &nbsp | &nbsp
+    📕 <a href="https://tech.kakao.com/posts/804">Teaser Blog</a> &nbsp
+</p>
+<br><br>
+
+## News 🔥
+
+- `2026/01/15`: 🤗 Released `kanana-2-30b-a3b-2601` HF model weights.
+- `2026/01/15`: 📕 Published blog posts ([pre-training](https://tech.kakao.com/posts/807), [post-training](https://tech.kakao.com/posts/808)) about the development of `Kanana-2` models.
+- `2025/12/19`: 🤗 Released `kanana-2-30b-a3b` HF model weights and publised a [teaser blog](https://tech.kakao.com/posts/804).
+
+<br>
+
+# Kanana-2 Highlights
+
+**Kanana-2**, the latest open-source evolution of the Kanana model family, is designed specifically for **Agentic AI**, presenting substantial enhancements in **tool calling, complex instruction following, and logical reasoning**. This new version adopts a cutting-edge architecture featuring MLA (Multi-head Latent Attention) and MoE (Mixture of Experts). These innovations allow the model to utilize significantly fewer active parameters compared to the previous 32.5B model while delivering superior performance and ensuring high throughput. Furthermore, the model **natively supports context lengths of up to 32,768 tokens**, enabling it to maintain coherence when handling extensive documents or long-context interactions.
+
+In addition, Kanana-2 now supports 6 languages, covering **Korean, English, Japanese, Chinese, Thai, and Vietnamese**. To support this expansion, Kanana-2 utilizes a newly trained tokenizer that demonstrates superior tokenization efficiency across these languages, including an improvement of over 30% specifically for Korean. Finally, to address advanced problem-solving needs, Kanana-2 introduces **reasoning models** capable of deliberate thinking and reasoning, achieving significantly enhanced performance in downstream tasks, especially when tackling hard problems.
+
+> [!NOTE]
+> No Kakao user data was used for either pre-training or post-training.
+
+<br>
+
+## Model Overview
+
+**kanana-2-30b-a3b** series has the following features:
+- Total Parameters: 30B
+- Activated Parameters: 3B
+- Number of Layers: 48
+- Number of Dense Layers: 1
+- Number of Experts: 128
+- Number of Selected Experts: 6
+- Number of Shared Experts: 2
+- Attention Mechanism: MLA
+- Vocabulary Size: 128256
+- Context Length: 32,768
+
+<br>
+
+## Model Downloads
+
+<div align="left">
+
+| **Model** | **Download** |
+| :------------: | :------------: |
+| kanana-2-30b-a3b-base-2601<sup>*</sup> | [🤗 HuggingFace](https://huggingface.co/kakaocorp/kanana-2-30b-a3b-base-2601)   |
+| kanana-2-30b-a3b-mid-2601<sup>*</sup>  | [🤗 HuggingFace](https://huggingface.co/kakaocorp/kanana-2-30b-a3b-mid-2601)   |
+| kanana-2-30b-a3b-instruct-2601 | [🤗 HuggingFace](https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601)   |
+| kanana-2-30b-a3b-thinking-2601 | [🤗 HuggingFace](https://huggingface.co/kakaocorp/kanana-2-30b-a3b-thinking-2601)   |
+
+<sub>
+<sup>*</sup> We are releasing the <code><small>kanana-2-30b-a3b-base-2601</small></code> (prior to mid-training) checkpoint to contribute to the research community.<br>
+&nbsp&nbspNote: <code><small>kanana-2-30b-a3b-mid-2601</small></code> is identical to <a href="https://huggingface.co/kakaocorp/kanana-2-30b-a3b-base">kanana-2-30b-a3b-base</a>.
+</sub>
+
+</div>
+
+<br>
+
+## Performance
+
+### Base model evaluation results
+
+
+<table>
+<thead>
+<tr>
+<th align="center">Benchmark</th>
+<th align="center">Metric</th>
+<th align="center">Shot</th>
+<th align="center">kanana-2-30b-a3b-mid-2601</th>
+<th align="center">kanana-2-30b-a3b-base-2601</th>
+<th align="center">kanana-1.5-32.5b-base</th>
+<th align="center">Qwen3-30B-A3B-Base<sup>*</sup></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center" colspan="7">General Tasks</td>
+</tr>
+<tr>
+<td align="center">MMLU</td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">75.44</td>
+<td align="center">74.83</td>
+<td align="center">76.76</td>
+<td align="center">81.14</td>
+</tr>
+<tr>
+<td align="center">MMLU-Pro</td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">56.14</td>
+<td align="center">52.61</td>
+<td align="center">52.40</td>
+<td align="center">61.83</td>
+</tr>
+<tr>
+<td align="center">BBH</td>
+<td align="center">acc</td>
+<td align="center">3</td>
+<td align="center">79.76</td>
+<td align="center">76.46</td>
+<td align="center">81.54</td>
+<td align="center">79.97</td>
+</tr>
+<tr>
+<td align="center">SimpleQA<sup>†</sup></td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">29.70</td>
+<td align="center">29.13</td>
+<td align="center">26.95</td>
+<td align="center">26.47</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Mathematics Tasks</td>
+</tr>
+<tr>
+<td align="center">MATH</td>
+<td align="center">em</td>
+<td align="center">4</td>
+<td align="center">54.40</td>
+<td align="center">48.86</td>
+<td align="center">47.68</td>
+<td align="center">62.58</td>
+</tr>
+<tr>
+<td align="center">GSM8K</td>
+<td align="center">em</td>
+<td align="center">8</td>
+<td align="center">82.71</td>
+<td align="center">76.57</td>
+<td align="center">85.14</td>
+<td align="center">88.10</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Coding Tasks</td>
+</tr>
+<tr>
+<td align="center">HumanEval</td>
+<td align="center">pass@1</td>
+<td align="center">0</td>
+<td align="center">75.29</td>
+<td align="center">71.34</td>
+<td align="center">75.59</td>
+<td align="center">53.32</td>
+</tr>
+<tr>
+<td align="center">MBPP</td>
+<td align="center">pass@1</td>
+<td align="center">3</td>
+<td align="center">62.39</td>
+<td align="center">60.21</td>
+<td align="center">65.96</td>
+<td align="center">72.58</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Korean Tasks</td>
+</tr>
+<tr>
+<td align="center">KMMLU</td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">62.15</td>
+<td align="center">61.98</td>
+<td align="center">61.56</td>
+<td align="center">62.25</td>
+</tr>
+<tr>
+<td align="center">KoSimpleQA<sup>†</sup></td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">49.70</td>
+<td align="center">49.40</td>
+<td align="center">45.70</td>
+<td align="center">26.33</td>
+</tr>
+<tr>
+<td align="center">HAE-RAE Bench (v1.0)</td>
+<td align="center">acc</td>
+<td align="center">5</td>
+<td align="center">88.73</td>
+<td align="center">88.91</td>
+<td align="center">90.65</td>
+<td align="center">72.04</td>
+</tr>
+<tr>
+<td align="center">MATH-Ko<sup>‡</sup></td>
+<td align="center">em</td>
+<td align="center">4</td>
+<td align="center">54.07</td>
+<td align="center">45.58</td>
+<td align="center">47.42</td>
+<td align="center">58.20</td>
+</tr>
+<tr>
+<td align="center">GSM8K-Ko<sup>‡</sup></td>
+<td align="center">em</td>
+<td align="center">8</td>
+<td align="center">77.48</td>
+<td align="center">70.43</td>
+<td align="center">81.43</td>
+<td align="center">88.10</td>
+</tr>
+<tr>
+<td align="center">MBPP-Ko<sup>§</sup></td>
+<td align="center">pass@1</td>
+<td align="center">3</td>
+<td align="center">61.55</td>
+<td align="center">57.29</td>
+<td align="center">65.41</td>
+<td align="center">66.84</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Long Context Tasks</td>
+</tr>
+<tr>
+<td align="center">RULER-4K</td>
+<td align="center">acc</td>
+<td align="center">0</td>
+<td align="center">93.09</td>
+<td align="center">92.49</td>
+<td align="center">86.39</td>
+<td align="center">94.32</td>
+</tr>
+<tr>
+<td align="center">RULER-8K</td>
+<td align="center">acc</td>
+<td align="center">0</td>
+<td align="center">92.29</td>
+<td align="center">92.14</td>
+<td align="center">90.16</td>
+<td align="center">92.16</td>
+</tr>
+<tr>
+<td align="center">RULER-16K</td>
+<td align="center">acc</td>
+<td align="center">0</td>
+<td align="center">90.73</td>
+<td align="center">90.01</td>
+<td align="center">85.88</td>
+<td align="center">91.28</td>
+</tr>
+<tr>
+<td align="center">RULER-32K</td>
+<td align="center">acc</td>
+<td align="center">0</td>
+<td align="center">88.63</td>
+<td align="center">87.92</td>
+<td align="center">81.62</td>
+<td align="center">88.32</td>
+</tr>
+</tbody>
+</table>
+
+<sub>
+<sup>*</sup> Evaluated using an internal evaluation toolkit.<br>
+<sup>†</sup> Evaluated in Multiple Choice Question Answering (MCQA) format with 10 options.<br>
+<sup>‡</sup> Subsets from <a href="https://huggingface.co/datasets/HAERAE-HUB/HRM8K">HRM8K</a> (MATH, GSM8K).<br>
+<sup>§</sup> Internally translated to Korean.
+</sub>
+
+<br>
+
+### Instruct model evaluation results
+
+<table>
+<thead>
+<tr>
+<th align="center">Benchmark</th>
+<th align="center">Metric</th>
+<th align="center">kanana-2-30b-a3b-instruct-2601</th>
+<th align="center">kanana-2-30b-a3b-instruct</th>
+<th align="center">kanana-1.5-32.5b-instruct</th>
+<th align="center">Qwen3-30B-A3B-Instruct-2507<sup>*</sup></th>
+<th align="center">Qwen3-30B-A3B<br>(non-thinking)<sup>*</sup></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center" colspan="7">Chat</td>
+</tr>
+<tr>
+<td align="center">MT-Bench</td>
+<td align="center">judge<sup>†</sup></td>
+<td align="center">8.30</td>
+<td align="center">8.42</td>
+<td align="center">8.23</td>
+<td align="center">8.71</td>
+<td align="center">8.38</td>
+</tr>
+<tr>
+<td align="center">KoMT-Bench</td>
+<td align="center">judge<sup>†</sup></td>
+<td align="center">8.21</td>
+<td align="center">8.24</td>
+<td align="center">7.94</td>
+<td align="center">8.49</td>
+<td align="center">7.89</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Instruction Following</td>
+</tr>
+<tr>
+<td align="center">IFEval</td>
+<td align="center">prompt strict</td>
+<td align="center">87.25</td>
+<td align="center">84.47</td>
+<td align="center">79.48</td>
+<td align="center">82.62</td>
+<td align="center">84.10</td>
+</tr>
+<tr>
+<td align="center">IFBench</td>
+<td align="center">prompt strict</td>
+<td align="center">48.30</td>
+<td align="center">41.84</td>
+<td align="center">38.78</td>
+<td align="center">30.27</td>
+<td align="center">29.25</td>
+</tr>
+<tr>
+<td align="center">Multi-IF (EN)</td>
+<td align="center">acc</td>
+<td align="center">77.88</td>
+<td align="center">75.81</td>
+<td align="center">68.51</td>
+<td align="center">77.93</td>
+<td align="center">81.03</td>
+</tr>
+<tr>
+<td align="center">Multi-Challenge</td>
+<td align="center">acc</td>
+<td align="center">35.16</td>
+<td align="center">34.80</td>
+<td align="center">19.05</td>
+<td align="center">41.76</td>
+<td align="center">27.84</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Tool Calling</td>
+</tr>
+<tr>
+<td align="center">BFCL-v3<br>(Live<sup>‡</sup>)</td>
+<td align="center">pass@1</td>
+<td align="center">76.66</td>
+<td align="center">74.30</td>
+<td align="center">68.74</td>
+<td align="center">73.93</td>
+<td align="center">69.14</td>
+</tr>
+<tr>
+<td align="center">BFCL-v3<br>(Multi-Turn<sup>‡</sup>)</td>
+<td align="center">pass@1</td>
+<td align="center">38.63</td>
+<td align="center">35.38</td>
+<td align="center">11.38</td>
+<td align="center">38.77</td>
+<td align="center">11.88</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Code Generation</td>
+</tr>
+<tr>
+<td align="center">HumanEval+</td>
+<td align="center">pass@1</td>
+<td align="center">81.10</td>
+<td align="center">79.88</td>
+<td align="center">79.88</td>
+<td align="center">86.59</td>
+<td align="center">87.20</td>
+</tr>
+<tr>
+<td align="center">MBPP+</td>
+<td align="center">pass@1</td>
+<td align="center">73.02</td>
+<td align="center">73.81</td>
+<td align="center">71.96</td>
+<td align="center">75.13</td>
+<td align="center">75.13</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Mathematics</td>
+</tr>
+<tr>
+<td align="center">GSM8K</td>
+<td align="center">em</td>
+<td align="center">93.10</td>
+<td align="center">91.89</td>
+<td align="center">91.58</td>
+<td align="center">93.56</td>
+<td align="center">93.33</td>
+</tr>
+<tr>
+<td align="center">MATH</td>
+<td align="center">acc</td>
+<td align="center">88.56</td>
+<td align="center">86.26</td>
+<td align="center">77.92</td>
+<td align="center">90.96</td>
+<td align="center">87.20</td>
+</tr>
+<tr>
+<td align="center" colspan="7">Reasoning & Knowledge</td>
+</tr>
+<tr>
+<td align="center">MMLU</td>
+<td align="center">em</td>
+<td align="center">81.61</td>
+<td align="center">80.80</td>
+<td align="center">82.75</td>
+<td align="center">87.13</td>
+<td align="center">85.60</td>
+</tr>
+<tr>
+<td align="center">KMMLU</td>
+<td align="center">em</td>
+<td align="center">68.26</td>
+<td align="center">67.32</td>
+<td align="center">65.75</td>
+<td align="center">67.56</td>
+<td align="center">63.49</td>
+</tr>
+<tr>
+<td align="center">GPQA Diamond</td>
+<td align="center">pass@1</td>
+<td align="center">52.53</td>
+<td align="center">42.93</td>
+<td align="center">42.42</td>
+<td align="center">54.55</td>
+<td align="center">50.51</td>
+</tr>
+<tr>
+<td align="center">HAERAE-Bench (v1.0)</td>
+<td align="center">em</td>
+<td align="center">75.57</td>
+<td align="center">75.57</td>
+<td align="center">65.34</td>
+<td align="center">53.41</td>
+<td align="center">57.39</td>
+</tr>
+</tbody>
+</table>
+
+<sub>
+<sup>*</sup> Evaluated using an internal evaluation toolkit.<br>
+<sup>†</sup> Evaluated using <code><small>gpt-4o-2024-08-06</small></code> as the judge model.<br>
+<sup>‡</sup> <code><small>Live</small></code> denotes the average score of 6 live benchmarks, and <code><small>Multi-Turn</small></code> denotes the average score of 4 multi-turn benchmarks.
+</sub>
+
+<br>
+
+### Reasoning model evaluation results
+
+<table>
+<thead>
+<tr>
+<th align="center">Benchmark</th>
+<th align="center">Metric</th>
+<th align="center">kanana-2-30b-a3b-thinking-2601</th>
+<th align="center">kanana-2-30b-a3b-thinking</th>
+<th align="center">Qwen3-30B-A3B-Thinking-2507<sup>*</sup></th>
+<th align="center">Qwen3-30B-A3B<br>(thinking)<sup>*</sup></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center" colspan="6">Reasoning & Knowledge</td>
+</tr>
+<tr>
+<td align="center">MMLU-Pro</td>
+<td align="center">pass@1</td>
+<td align="center">74.2</td>
+<td align="center">75.3</td>
+<td align="center">80.8</td>
+<td align="center">78.5</td>
+</tr>
+<tr>
+<td align="center">GPQA Diamond</td>
+<td align="center">pass@1</td>
+<td align="center">57.8</td>
+<td align="center">61.3</td>
+<td align="center">70.6</td>
+<td align="center">62.6</td>
+</tr>
+<tr>
+<td align="center" colspan="6">Competition Math</td>
+</tr>
+<tr>
+<td align="center">AIME 2025</td>
+<td align="center">pass@1</td>
+<td align="center">74.0</td>
+<td align="center">72.7</td>
+<td align="center">82.3</td>
+<td align="center">70.7</td>
+</tr>
+<tr>
+<td align="center">AIME 2024</td>
+<td align="center">pass@1</td>
+<td align="center">79.0</td>
+<td align="center">78.3</td>
+<td align="center">91.0</td>
+<td align="center">82.7</td>
+</tr>
+<tr>
+<td align="center">AIME 2024-Ko<sup>†</sup></td>
+<td align="center">pass@1</td>
+<td align="center">75.0</td>
+<td align="center">25.3</td>
+<td align="center">80.3</td>
+<td align="center">72.3</td>
+</tr>
+<tr>
+<td align="center" colspan="6">Code Generation</td>
+</tr>
+<tr>
+<td align="center">LiveCodeBench</td>
+<td align="center">pass@1</td>
+<td align="center">58.8</td>
+<td align="center">60.8</td>
+<td align="center">68.3</td>
+<td align="center">62.3</td>
+</tr>
+<tr>
+<td align="center">LiveCodeBench-Ko<sup>‡</sup></td>
+<td align="center">pass@1</td>
+<td align="center">51.2</td>
+<td align="center">9.4</td>
+<td align="center">66.3<sup>¶</sup></td>
+<td align="center">61.5<sup>¶</sup></td>
+</tr>
+<tr>
+<td align="center" colspan="6">Instruction Following</td>
+</tr>
+<tr>
+<td align="center">IFEval</td>
+<td align="center">prompt strict</td>
+<td align="center">82.2</td>
+<td align="center">82.2</td>
+<td align="center">87.8</td>
+<td align="center">86.1</td>
+</tr>
+<tr>
+<td align="center">IFBench</td>
+<td align="center">prompt strict</td>
+<td align="center">47.8</td>
+<td align="center">42.3</td>
+<td align="center">47.6</td>
+<td align="center">36.7</td>
+</tr>
+<tr>
+<td align="center" colspan="6">Tool Calling</td>
+</tr>
+<tr>
+<td align="center">BFCL-v3<br>(Live<sup>§</sup>)</td>
+<td align="center">pass@1</td>
+<td align="center">75.9</td>
+<td align="center">75.6</td>
+<td align="center">82.9</td>
+<td align="center">80.3</td>
+</tr>
+<tr>
+<td align="center">BFCL-v3<br>(Multi-Turn<sup>§</sup>)</td>
+<td align="center">pass@1</td>
+<td align="center">43.7</td>
+<td align="center">34.3</td>
+<td align="center">53.6</td>
+<td align="center">35.6</td>
+</tr>
+</tbody>
+</table>
+
+<sub>
+<sup>*</sup> Evaluated using an internal evaluation toolkit.<br>
+<sup>†</sup> Korean translation of AIME 2024 sourced from <a href="https://huggingface.co/datasets/amphora/MCLM">MCLM</a>.<br>
+<sup>‡</sup> Internally translated to Korean.<br>
+<sup>§</sup> <code><small>Live</small></code> denotes the average score of 6 live benchmarks, and <code><small>Multi-Turn</small></code> denotes the average score of 4 multi-turn benchmarks.<br>
+<sup>¶</sup> Most responses were generated in English.
+</sub>
+
+<br>
+
+## Deployment
+
+> [!NOTE]
+> For optimal results with the reasoning model, please adhere to the default parameters: `temperature=0.6`, `top_p=0.95`, `top_k=20`. **We strongly advise against greedy decoding**, as it may lead to performance degradation and infinite repetition loops.
+
+### vLLM
+[vLLM](https://github.com/vllm-project/vllm) is a fast and memory-optimized engine designed for high-performance LLM inference and serving.
+
+For kanana-2-30b-a3b-instruct-2601,
+```shell
+vllm serve kakaocorp/kanana-2-30b-a3b-instruct-2601 --enable-auto-tool-choice --tool-call-parser hermes
+```
+
+For kanana-2-30b-a3b-thinking-2601,
+```shell
+vllm serve kakaocorp/kanana-2-30b-a3b-thinking-2601 --reasoning-parser deepseek_r1 --enable-auto-tool-choice --tool-call-parser hermes
+```
+
+### SGLang
+[SGLang](https://github.com/sgl-project/sglang) is a high-efficiency framework for serving LLMs and VLMs, enabling easy deployment of OpenAI-compatible API servers.
+
+For kanana-2-30b-a3b-instruct-2601,
+```shell
+python3 -m sglang.launch_server --model-path kakaocorp/kanana-2-30b-a3b-instruct-2601 --tool-call-parser qwen
+```
+
+For kanana-2-30b-a3b-thinking-2601,
+```shell
+python3 -m sglang.launch_server --model-path kakaocorp/kanana-2-30b-a3b-thinking-2601 --reasoning-parser deepseek-r1 --tool-call-parser qwen
+```
+
+<br>
+
+## Processing 32K+ Length
+Currently, the `config.json` uploaded to HuggingFace is configured for token lengths of 32,768 or less. To process tokens beyond this length, YaRN must be applied. By updating the `config.json` with the following parameters, you can apply YaRN to handle token sequences up to 128K in length:
+```json
+"rope_scaling": {
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 4.0,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 32768,
+    "type": "yarn",
+},
+```
+
+Passing command line arguments for deployment:
+
+- `vllm`
+    ```shell
+    vllm serve ... --hf-overrides '{"max_position_embeddings": 131072, "rope_scaling": {"rope_type":"deepseek_yarn","factor":4.0,"beta_fast":32,"beta_slow":1,"mscale":1.0,"mscale_all_dim":1.0,"original_max_position_embeddings":32768}}'
+    ```
+
+- `sglang`
+    ```shell
+    python3 -m sglang.launch_server ... --json-model-override-args '{"max_position_embeddings":131072, "rope_scaling":{"rope_type":"deepseek_yarn","factor":4.0,"beta_fast":32,"beta_slow":1,"mscale":1.0,"mscale_all_dim":1.0,"original_max_position_embeddings":32768}}'
+    ```
+
+> [!NOTE]
+> Most leading open-source implementations of static YaRN apply a constant scaling factor, which can negatively impact performance on shorter texts. To ensure optimal performance:
+> * **Enable `rope_scaling` only when necessary** for processing long contexts.
+> * **Adjust the `factor` based on your specific needs** (e.g., set `factor` to 2.0 for a 65,536-token context)."
+
+<br>
+
+## License
+
+The model weights are released under the [Kanana License](./LICENSE).
+
+<br>
+
+## Citation
+
+```
+@article{,
+  title={Kanana-2 LLM},
+  author={Kanana LLM},
+  year={2025},
+  url={https://huggingface.co/collections/kakaocorp/kanana-2}
+}
+```
+
+<br>
+
+## Contact
+- Kanana LLM Team Technical Support: kanana-llm@kakaocorp.com
+- Business & Partnership Contact: alpha.k@kakaocorp.com
--- a/assets/logo/kanana.png
+++ b/assets/logo/kanana.png
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,85 @@
+{%- if not add_generation_prompt is defined %}
+    {%- set add_generation_prompt = false %}
+{%- endif %}
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index and (loop.last or reasoning_content) and reasoning_content|trim != '' %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,48 @@
+{
+  "architectures": [
+    "DeepseekV3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128010,
+  "first_k_dense_replace": 1,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "kv_lora_rank": 512,
+  "max_position_embeddings": 32768,
+  "model_type": "deepseek_v3",
+  "moe_intermediate_size": 768,
+  "moe_layer_freq": 1,
+  "n_group": 1,
+  "n_routed_experts": 128,
+  "n_shared_experts": 2,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts_per_tok": 6,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 32,
+  "pad_token_id": 128001,
+  "pretraining_tp": 1,
+  "q_lora_rank": null,
+  "qk_head_dim": 192,
+  "qk_nope_head_dim": 128,
+  "qk_rope_head_dim": 64,
+  "rms_norm_eps": 1e-06,
+  "rope_interleave": true,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "routed_scaling_factor": 2.448,
+  "scoring_func": "sigmoid",
+  "tie_word_embeddings": false,
+  "topk_group": 1,
+  "topk_method": "noaux_tc",
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "v_head_dim": 128,
+  "vocab_size": 128256
+}
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128010,
+  "pad_token_id": 128001,
+  "transformers_version": "4.57.3"
+}
--- a/model-00001-of-00013.safetensors
+++ b/model-00001-of-00013.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:254d64c4471e0287e48f3cb30fe7b7c804906d4950112136feaf1760294414cc
+size 4999552848
--- a/model-00002-of-00013.safetensors
+++ b/model-00002-of-00013.safetensors
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