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Model: KOREAson/KO-REAson-KL3_1-8B-0831 Source: Original Platform
2026-06-02 03:06:14 +08:00
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+---
+library_name: transformers
+tags: []
+---
+
+# KO-REAson
+
+**KO-REAson** is a series of Korean-centric reasoning language models developed in collaboration with [OneLineAI](https://onelineai.com/), [KISTI-KONI](https://huggingface.co/KISTI-KONI), [HAE-RAE](https://huggingface.co/HAERAE-HUB) and ORACLE.  
+
+We use the **Language-Mixed Chain-of-Thought (CoT)** approach, which allows the model to alternate between English and Korean during the “Think” stage of reasoning, preserving key Korean terms while leveraging English for logical scaffolding.  
+
+Top-performing models of our series [KO-REAson-AX3_1-7B-0831 (KONI-7B-R-20250831)](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831) and [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831) show performance comparable to models trained on closed-source datasets such as Exaone-Deep-7.8B.
+
+
+<p align="left">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/60d3e619b8448e1785bbda2a/uqrKdxbQEqAFknYBmuH7Y.png" 
+       alt="Model Comparison" width="750"/>
+  <br>
+  <em style="display:inline-block; max-width:750px; text-align:cener; white-space:normal; word-wrap:break-word; line-height:1.5;">
+    <b>Left:</b> Average performance (Held-out-Ko) of open models trained on closed or open data; 
+    our models are highlighted in green.
+  </em>
+</p>
+
+## Model Details
+
+The **KO-REAson-0831** family comes in six variants based on the base model used.
+
+| Model (link)                                                                                 | Base           | Notes                       |
+| -------------------------------------------------------------------------------------------- | -------------------- | --------------------------- |
+| [KO-REAson-L3_1-8B-0831](https://huggingface.co/KoReason/KO-REASon-L3_1-8B-0831)   | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)      | `L3_1` → Llama-3.1-8B       |
+| [KO-REAson-KL3_1-8B-0831](https://huggingface.co/KOREAson/KO-REAson-KL3_1-8B-0831) | [Koni-Llama-3.1-8B](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024)    | `KL3_1` → Koni-Llama-3.1-8B; also called [KONI-Llama3.1-8B-R-20250831](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-R-20250831) |
+| [KO-REAson-G3-4B-0831](https://huggingface.co/KoReason/KO-REASon-G3-4B-0831)        | [Gemma-3 4B](https://huggingface.co/google/gemma-3-4b-it)           | `G3` → Gemma-3-4B           |
+| [KO-REAson-AX3_1-7B-0831](https://huggingface.co/KOREAson/KO-REAson-7B-AX3_1-0831) | [A.X.-3.1-Light (≈7B)](https://huggingface.co/skt/A.X-3.1-Light) | `AX3_1` → A.X.-3.1-Light; also called [KONI-7B-R-20250831](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831)  |
+| [KO-REAson-K2505_8B-0831](https://huggingface.co/KoReason/KO-REASon-K2505_8B-0831) | [Kanana-2505 (8B)](https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505)     | `K2505` → Kanana-2505       |
+| [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831)   | [Qwen-2.5 (7B)](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)        | `Q2_5` → Qwen-2.5           |
+
+
+
+# Performance
+
+**Evaluation Datasets**
+
+The model's performance was evaluated across a total of 11 benchmarks, and the evaluation suite is divided into two parts: (You can check these benchmarks in [HAERAE-HUB/KoSimpleEval](https://huggingface.co/datasets/HAERAE-HUB/KoSimpleEval))
+
+- **Held-in**: This set of benchmarks is used for routine monitoring of the model's performance during the training and ablation study phases.
+- **Held-out**: This set is used only once to evaluate the final model after all training and ablations are complete.
+
+This separation is designed to prevent inadvertent overfitting to the benchmarks during the iterative training process and to provide a more accurate measure of the model's generalization capabilities.
+
+|**Category**|**Held-in**|**Held-out**|
+|---|---|---|
+|**General Knowledge**|KMMLU-Redux|KMMLU-HARD, KMMLU-Pro|
+|**Reasoning**|MCLM|KSM, GPQA, AIME2024, AIME2025|
+|**Korean-specific**|HAE-RAE Bench|CLIcK, KoBALT-700|
+
+
+**Comparison with models trained on public datasets**
+
+<table>
+  <thead>
+    <tr>
+      <th>Models</th>
+      <th># Instances</th>
+      <th>Methodology</th>
+      <th>Held-Out (Ko)</th>
+      <th>Held-Out (En)</th>
+      <th>Total</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>KO-REASon-AX3_1-7B-0831(KONI-7B-R-20250831; Ours)</th>
+      <td>260k</td>
+      <td>SFT</td>
+      <td><b>44.6</b></td>
+      <td>41.2</td>
+      <td><u>43.3</u></td>
+    </tr>
+    <tr>
+      <th>KO-REASon-7B-Q2_5-0831(Ours)</th>
+      <td>260k</td>
+      <td>SFT</td>
+      <td><b>45.10</b></td>
+      <td>38.75</td>
+      <td><u>49.95</u></td>
+    </tr>
+    <tr>
+      <th>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</th>
+      <td>260k</td>
+      <td>SFT</td>
+      <td>40.13</td>
+      <td>30.57</td>
+      <td>43.66</td>
+    </tr>
+    <tr>
+      <td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (En)</td>
+    </tr>
+    <tr>
+      <th>OpenThinker3-7B</th>
+      <td>1.2M</td>
+      <td>SFT</td>
+      <td>33.6</td>
+      <td><b>55.5</b></td>
+      <td>41.8</td>
+    </tr>
+    <tr>
+      <th>s1.1-7B</th>
+      <td>1k</td>
+      <td>SFT</td>
+      <td>35.6</td>
+      <td>23.4</td>
+      <td>31.1</td>
+    </tr>
+    <tr>
+      <th>Llama-3.1-Nemotron-Nano-8B-v1</th>
+      <td>&gt;3M</td>
+      <td>SFT &amp; RL</td>
+      <td>27.0</td>
+      <td>44.1</td>
+      <td>33.4</td>
+    </tr>
+    <tr>
+      <td colspan="6" style="text-align:center; font-weight:bold;">Open Recipe (Ko)</td>
+    </tr>
+    <tr>
+      <th>Ko-R1-14B</th>
+      <td>45k</td>
+      <td>SFT</td>
+      <td><u>43.7</u></td>
+      <td><u>46.3</u></td>
+      <td><b>44.7</b></td>
+    </tr>
+    <tr>
+      <th>Ko-R1-7B</th>
+      <td>45k</td>
+      <td>SFT</td>
+      <td>27.3</td>
+      <td>36.1</td>
+      <td>30.6</td>
+    </tr>
+    <tr>
+      <th>LLaMa-3.1-Ko-Reasoning-8B</th>
+      <td>63k</td>
+      <td>SFT</td>
+      <td>17.7</td>
+      <td>7.7</td>
+      <td>14.0</td>
+    </tr>
+  </tbody>
+</table>
+
+**Held-out benchmark performance**
+
+<table border="1" cellspacing="0" cellpadding="6">
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th rowspan="2">Model Size</th>
+      <th colspan="2">General</th>
+      <th colspan="4">Reasoning</th>
+      <th colspan="2">Korean-Specific</th>
+      <th rowspan="2">Average<br>(Held-out)</th>
+      <th rowspan="2">Average<br>(Held-out-Ko)</th>
+    </tr>
+    <tr>
+      <th>KMMLU-HARD</th>
+      <th>KMMLU-Pro</th>
+      <th>KSM</th>
+      <th>AIME 2024</th>
+      <th>AIME 2025</th>
+      <th>GPQA</th>
+      <th>CLIcK</th>
+      <th>KoBALT-700</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><b>Llama-3.1-Nemotron-Nano-8B</b></td>
+      <td>8.03</td><td>21.47</td><td>22.89</td><td>47.06</td><td>56.67</td><td>43.33</td><td>32.32</td><td>34.54</td><td>9.29</td><td>33.45</td><td>27.05</td>
+    </tr>
+    <tr>
+      <td><b>Llama-3.1-Korean-Reasoning-8B-Instruct</b></td>
+      <td>8.03</td><td>14.91</td><td>21.72</td><td>6.09</td><td>0.00</td><td>0.00</td><td>23.23</td><td>39.65</td><td>6.14</td><td>13.97</td><td>17.70</td>
+    </tr>
+    <tr>
+      <td><b>EXAONE-Deep-7.8B</b></td>
+      <td>7.82</td><td><u>40.96</u></td><td>37.35</td><td><b>70.80</b></td><td><b>70.00</b></td><td><b>63.33</b></td><td><b>64.65</b></td><td>54.24</td><td>18.86</td><td><b>52.52</b></td><td>44.44</td>
+    </tr>
+    <tr>
+      <td><b>DeepSeek-R1-Distill-Qwen-7B</b></td>
+      <td>7.62</td><td>0.00</td><td>23.00</td><td>56.09</td><td>60.00</td><td>40.00</td><td>43.43</td><td>0.00</td><td>8.29</td><td>28.85</td><td>17.48</td>
+    </tr>
+    <tr>
+      <td><b>DeepSeek-R1-Distill-Llama-8B</b></td>
+      <td>8.03</td><td>23.22</td><td>26.26</td><td>29.97</td><td>33.33</td><td>20.00</td><td><U>46.46</u></td><td>39.05</td><td>13.29</td><td>28.95</td><td>26.36</td>
+    </tr>
+    <tr>
+      <td><b>s1.1-7B</b></td>
+      <td>7.62</td><td>31.16</td><td><u>37.70</u></td><td>30.60</td><td>16.67</td><td>23.33</td><td>30.30</td><td><u>56.84</u></td><td><u>21.86</u></td><td>31.06</td><td>35.63</td>
+    </tr>
+    <tr>
+      <td><b>OpenThinker3-7B</b></td>
+      <td>7.62</td><td>30.31</td><td>26.26</td><td><u>63.59</u></td><td><u>66.67</u></td><td><u>53.33</u></td><td><u>46.46</u></td><td>47.69</td><td>10.14</td><td>35.63</td><td>30.60</td>
+    </tr>
+    <tr>
+      <td><b>Ko-R1-7B</b></td>
+      <td>7.61</td><td>28.46</td><td>19.31</td><td>51.61</td><td>46.67</td><td>33.33</td><td>28.28</td><td>32.48</td><td>4.71</td><td>30.61</td><td>27.31</td>
+    </tr>
+    <tr>
+      <td><b>KO-REAson-KL3_1-8B-0831(KONI-Llama3.1-8B-R-20250831)</b></td>
+      <td>8.03</td><td>44.64</td><td>40.08</td><td>37.96</td><td>23.33</td><td>30.00</td><td>38.38</td><td>56.39</td><td>21.57</td><td>30.57</td><td>40.13</td>
+    </tr>
+    <tr>
+      <td><b>KO-REASon-AX3_1-7B-0831 (KONI-7B-R-20250831)</b></td>
+      <td>7.26</td><td>45.57</td><td>38.13</td><td>52.80</td><td>53.33</td><td>33.33</td><td>36.87</td><td><b>62.86</b></td><td>23.43</td><td><u>43.29</u></td><td><u>44.56</u></td>
+    </tr>
+    <tr>
+      <td><b>KO-REASon-7B-Q2_5-0831</b></td>
+      <td>7.26</td><td><b>46.81</b></td><td><b>44.93</b></td><td>48.11</td><td>43.33</td><td>30.00</td><td>42.93</td><td>60.65</td><td><b>25.00</b></td><td>42.72</td><td><b>45.10</b></td>
+    </tr>
+  </tbody>
+</table>
+
+
+## Citation
+```
+The paper will be released soon!
+```
+
+
+## Contact
+
+For any questions contact us via the following email :)
+
+```
+spthsrbwls123@yonsei.ac.kr
+```
+
+
+## Acknowlegments
+This research was supported by the Korea Institute of Science and Technology Information (KISTI) (No.(KISTI) K25L1M1C1), aimed at developing KONI (KISTI Open Neural Intelligence), a large language model specialized in science and technology.
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,114 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "27 Aug 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "You are KONI, an AI assistant trained based on LlaMA3.1 and created by KISTI to be helpful and honest. Your knowledge spans a wide range of topics, allowing you to engage in substantive conversations and provide analysis on complex subjects. Below is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\n" }}
+{{- "Cutting Knowledge Date: July 2024\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {%- if (message.role == 'assistant') %}
+                {{- '<|start_header_id|>' + 'KONI' + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+        {%- else %}
+                {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+        {%- endif %}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' }}
+{%- endif %}
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--- a/configuration.json
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--- a/generation_config.json
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+}
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
				`@@ -0,0 +1 @@`
				`{"framework": "pytorch", "task": "text-generation", "allow_remote": true}`