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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.

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{{- 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 = "26 Jul 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 %}
{{- "Cutting Knowledge Date: December 2023\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) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- 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|>assistant<|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|>assistant<|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|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.55.4",
"use_cache": true,
"vocab_size": 128256
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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"do_sample": true,
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],
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "4.55.4"
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