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MIT License
Copyright (c) 2025 KT Corporation
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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---
license: mit
language:
- en
- ko
tags:
- KT
- K-intelligence
- Mi:dm
pipeline_tag: text-generation
base_model:
- K-intelligence/Midm-2.0-Mini-Instruct
---
<p align="center">
<picture>
<img src="https://i.imgur.com/RdZhPeZ.png" width="45%" style="margin: 40px auto;">
</picture>
</p>
<p align="center">
🤗 <a href="https://huggingface.co/collections/K-intelligence/mi-dm-20-6866406c301e5f45a6926af8">Mi:dm 2.0 Models</a> |
📜 <a href="https://github.com/K-intelligence-Midm/Midm-2.0/blob/main/Mi_dm2_0_technical_report.pdf">Mi:dm 2.0 Technical Report</a> |
📕 Mi:dm 2.0 Technical Blog*
</p>
<p align="center"><sub>*To be released soon</sub></p>
<br>
## News 📢
- 🔜 _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._
- ⚡️`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face🤗.
<br>
<br>
# Table of Contents
- ___Overview___
- [Mi:dm 2.0](#midm-20)
- [Quickstart](#quickstart)
- [Evaluation](#evaluation)
- ___Usage___
- [Run on Friendly.AI](#run-on-friendliai)
- [Run on Your Local Machine](#run-on-your-local-machine)
- [Deployment](#deployment)
- [Tutorials](#tutorials)
- ___More Information___
- [Limitation](#limitation)
- [License](#license)
- [Contact](#contact)
<br>
<br>
# Overview
### Mi:dm 2.0
**Mi:dm 2.0** is a __"Korea-centric AI"__ model developed using KT's proprietary technology. The term __"Korea-centric AI"__ refers to a model that deeply internalizes the unique values, cognitive frameworks, and commonsense reasoning inherent to Korean society. It goes beyond simply processing or generating Korean text—it reflects a deeper understanding of the socio-cultural norms and values that define Korean society.
Mi:dm 2.0 is released in two versions:
- **Mi:dm 2.0 Base**
An 11.5B parameter dense model designed to balance model size and performance.
It extends an 8B-scale model by applying the Depth-up Scaling (DuS) method, making it suitable for real-world applications that require both performance and versatility.
- **Mi:dm 2.0 Mini**
A lightweight 2.3B parameter dense model optimized for on-device environments and systems with limited GPU resources.
It was derived from the Base model through pruning and distillation to enable compact deployment.
> [!Note]
> Neither the pre-training nor the post-training data includes KT users' data.
<br>
### Quickstart
Here is the code snippet to run conversational inference with the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_name = "K-intelligence/Midm-2.0-Mini-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
generation_config = GenerationConfig.from_pretrained(model_name)
prompt = "KT에 대해 소개해줘"
# message for inference
messages = [
{"role": "system",
"content": "Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다."},
{"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
output = model.generate(
input_ids.to("cuda"),
generation_config=generation_config,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=128,
do_sample=False,
)
print(tokenizer.decode(output[0]))
```
> [!NOTE]
> The `transformers` library should be version `4.45.0` or higher.
<br>
<br>
# Evaluation
#### Korean
<!-- first half table-->
<table>
<tr>
<th rowspan="2">Model</th>
<th colspan="5" align="center">Society & Culture</th>
<th colspan="3" align="center">General Knowledge</th>
<th colspan="3" align="center">Instruction Following</th>
</tr>
<tr>
<th align="center">K-Refer<sup>*</sup></th>
<th align="center">K-Refer-Hard<sup>*</sup></th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">HAERAE</th>
<th align="center">Avg.</th>
<th align="center">KMMLU</th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">Avg.</th>
<th align="center">Ko-IFEval</th>
<th align="center">Ko-MTBench</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center">53.6</td>
<td align="center">42.9</td>
<td align="center">35.8</td>
<td align="center">50.6</td>
<td align="center">45.7</td>
<td align="center"><strong>50.6</strong></td>
<td align="center"><strong>42.5</strong></td>
<td align="center"><strong>46.5</strong></td>
<td align="center"><strong>75.9</strong></td>
<td align="center">63.0</td>
<td align="center">69.4</td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center">64.0</td>
<td align="center"><strong>67.1</strong></td>
<td align="center"><strong>44.4</strong></td>
<td align="center">61.3</td>
<td align="center"><strong>59.2</strong></td>
<td align="center">43.5</td>
<td align="center">42.4</td>
<td align="center">43.0</td>
<td align="center">65.4</td>
<td align="center"><strong>74.0</strong></td>
<td align="center">68.9</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center"><strong>66.4</strong></td>
<td align="center">61.4</td>
<td align="center">36.7</td>
<td align="center"><strong>70.8</strong></td>
<td align="center">58.8</td>
<td align="center">45.1</td>
<td align="center">42.4</td>
<td align="center">43.8</td>
<td align="center">73.3</td>
<td align="center"><strong>74.0</strong></td>
<td align="center"><strong>73.6</strong></td>
</tr>
<!-- Spacer row -->
<tr><td colspan="13"> </td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center">72.4</td>
<td align="center">65.7</td>
<td align="center">49.8</td>
<td align="center">68.4</td>
<td align="center">64.1</td>
<td align="center">55.4</td>
<td align="center">54.7</td>
<td align="center">55.1</td>
<td align="center"><strong>83.6</strong></td>
<td align="center">71</td>
<td align="center">77.3</td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">43.2</td>
<td align="center">36.4</td>
<td align="center">33.8</td>
<td align="center">49.5</td>
<td align="center">40.7</td>
<td align="center">33.0</td>
<td align="center">36.7</td>
<td align="center">34.8</td>
<td align="center">60.1</td>
<td align="center">57</td>
<td align="center">58.5</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">71.6</td>
<td align="center">69.3</td>
<td align="center">46.9</td>
<td align="center">72.9</td>
<td align="center">65.2</td>
<td align="center">52.6</td>
<td align="center">45.6</td>
<td align="center">49.1</td>
<td align="center">69.1</td>
<td align="center">79.6</td>
<td align="center">74.4</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center"><strong>89.6</strong></td>
<td align="center"><strong>86.4</strong></td>
<td align="center"><strong>56.3</strong></td>
<td align="center"><strong>81.5</strong></td>
<td align="center"><strong>78.4</strong></td>
<td align="center"><strong>57.3</strong></td>
<td align="center"><strong>58.0</strong></td>
<td align="center"><strong>57.7</strong></td>
<td align="center">82</td>
<td align="center"><strong>89.7</strong></td>
<td align="center"><strong>85.9</strong></td>
</tr>
</table>
<!-- second half table-->
<table>
<tr>
<th rowspan="2" align="center">Model</th>
<th colspan="5" align="center">Comprehension</th>
<th colspan="5" align="center">Reasoning</th>
</tr>
<tr>
<th align="center">K-Prag<sup>*</sup></th>
<th align="center">K-Refer-Hard<sup>*</sup></th>
<th align="center">Ko-Best</th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">Avg.</th>
<th align="center">Ko-Winogrande</th>
<th align="center">Ko-Best</th>
<th align="center">LogicKor</th>
<th align="center">HRM8K</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center"><strong>73.9<strong></td>
<td align="center">56.7</td>
<td align="center"><strong>91.5</strong></td>
<td align="center"><strong>43.5</strong></td>
<td align="center"><strong>66.6</strong></td>
<td align="center"><strong>67.5</strong></td>
<td align="center"><strong>69.2</strong></td>
<td align="center">5.6</td>
<td align="center"><strong>56.7</strong></td>
<td align="center"><strong>43.8</strong></td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center">68.7</td>
<td align="center"><strong>58.5</strong></td>
<td align="center">87.2</td>
<td align="center">38.0</td>
<td align="center">62.5</td>
<td align="center">60.3</td>
<td align="center">64.1</td>
<td align="center">7.4</td>
<td align="center">38.5</td>
<td align="center">36.7</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center">69.5</td>
<td align="center">55.4</td>
<td align="center">80.5</td>
<td align="center">42.5</td>
<td align="center">61.9</td>
<td align="center">61.7</td>
<td align="center">64.5</td>
<td align="center"><strong>7.7</strong></td>
<td align="center">39.9</td>
<td align="center">37.4</td>
</tr>
<!-- Visual Spacer -->
<tr><td colspan="11"> </td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center"><strong>86.7</strong></td>
<td align="center"><strong>74.0</strong></td>
<td align="center">93.9</td>
<td align="center">52.0</td>
<td align="center"><strong>76.8</strong></td>
<td align="center"><strong>77.2</strong></td>
<td align="center"><strong>75.4</strong></td>
<td align="center">6.4</td>
<td align="center"><strong>64.5</strong></td>
<td align="center"><strong>48.8</strong></td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">59.9</td>
<td align="center">48.6</td>
<td align="center">77.4</td>
<td align="center">31.5</td>
<td align="center">51.5</td>
<td align="center">40.1</td>
<td align="center">26.0</td>
<td align="center">2.4</td>
<td align="center">30.9</td>
<td align="center">19.8</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">73.5</td>
<td align="center">61.9</td>
<td align="center">92.0</td>
<td align="center">44.0</td>
<td align="center">67.2</td>
<td align="center">64.6</td>
<td align="center">60.3</td>
<td align="center"><strong>8.6</strong></td>
<td align="center">49.7</td>
<td align="center">39.5</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center">86.5</td>
<td align="center">70.8</td>
<td align="center"><strong>95.2</strong></td>
<td align="center"><strong>53.0</strong></td>
<td align="center">76.1</td>
<td align="center">75.1</td>
<td align="center">73.0</td>
<td align="center"><strong>8.6</strong></td>
<td align="center">52.9</td>
<td align="center">44.8</td>
</tr>
</table>
`*` indicates KT proprietary evaluation resources.
<br>
#### English
<table>
<tr>
<th rowspan="2" align="center">Model</th>
<th align="center">Instruction</th>
<th colspan="4" align="center">Reasoning</th>
<th align="center">Math</th>
<th align="center">Coding</th>
<th colspan="3" align="center">General Knowledge</th>
</tr>
<tr>
<th align="center">IFEval</th>
<th align="center">BBH</th>
<th align="center">GPQA</th>
<th align="center">MuSR</th>
<th align="center">Avg.</th>
<th align="center">GSM8K</th>
<th align="center">MBPP+</th>
<th align="center">MMLU-pro</th>
<th align="center">MMLU</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center">79.7</td>
<td align="center"><strong>79.0</strong></td>
<td align="center"><strong>39.8</strong></td>
<td align="center"><strong>58.5</strong></td>
<td align="center"><strong>59.1</strong></td>
<td align="center"><strong>90.4</strong></td>
<td align="center">62.4</td>
<td align="center">-</td>
<td align="center"><strong>73.3</strong></td>
<td align="center"><strong>73.3</strong></td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center"><strong>81.1</strong></td>
<td align="center">46.4</td>
<td align="center">28.1</td>
<td align="center">49.7</td>
<td align="center">41.4</td>
<td align="center">82.5</td>
<td align="center">59.8</td>
<td align="center">-</td>
<td align="center">59.5</td>
<td align="center">59.5</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center">73.6</td>
<td align="center">44.5</td>
<td align="center">26.6</td>
<td align="center">51.7</td>
<td align="center">40.9</td>
<td align="center">83.1</td>
<td align="center"><strong>60.9</strong></td>
<td align="center">-</td>
<td align="center">56.5</td>
<td align="center">56.5</td>
</tr>
<tr><td colspan="11">&nbsp;</td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center">83.9</td>
<td align="center"><strong>83.4</strong></td>
<td align="center"><strong>49.8</strong></td>
<td align="center"><strong>57.7</strong></td>
<td align="center"><strong>63.6</strong></td>
<td align="center">88.0</td>
<td align="center">73.4</td>
<td align="center"><strong>70.5</strong></td>
<td align="center"><strong>82.7</strong></td>
<td align="center"><strong>76.6</strong></td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">79.9</td>
<td align="center">60.3</td>
<td align="center">21.6</td>
<td align="center">50.3</td>
<td align="center">44.1</td>
<td align="center">81.2</td>
<td align="center"><strong>81.8</strong></td>
<td align="center">47.6</td>
<td align="center">70.7</td>
<td align="center">59.2</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">83.6</td>
<td align="center">50.1</td>
<td align="center">33.1</td>
<td align="center">51.2</td>
<td align="center">44.8</td>
<td align="center">81.1</td>
<td align="center">79.4</td>
<td align="center">40.7</td>
<td align="center">69.0</td>
<td align="center">54.8</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center"><strong>84.0</strong></td>
<td align="center">77.7</td>
<td align="center">33.5</td>
<td align="center">51.9</td>
<td align="center">54.4</td>
<td align="center"><strong>91.6</strong></td>
<td align="center">77.5</td>
<td align="center">53.3</td>
<td align="center">73.7</td>
<td align="center">63.5</td>
</tr>
</table>
<br>
# Usage
### Run on Friendli.AI
You can try our model immediately via `Friendli.AI`. Simply click `Deploy` and then `Friendli Endpoints`.
> [!Note]
> Please note that a login to `Friendli.AI` is required after your fifth chat interaction.
<p>
<img src="./assets/image_1.png" alt="Left Image" width="36%" style="display:inline-block; margin-right:2%">
<img src="./assets/image_2.png" alt="Right Image" width="36%" style="display:inline-block">
</p>
### Run on Your Local Machine
We provide a detailed description about running Mi:dm 2.0 on your local machine using llama.cpp, LM Studio, and Ollama. Please check our [github](https://github.com/K-intelligence-Midm/Midm-2.0) for more information
### Deployment
To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API:
```bash
vllm serve K-intelligence/Midm-2.0-Mini-Instruct
```
### Tutorials
To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github](https://github.com/K-intelligence-Midm/Midm-2.0).
<br>
<br>
<br>
# More Information
### Limitation
* The training data for both Mi:dm 2.0 models consists primarily of English and Korean. Understanding and generation in other languages are not guaranteed.
* The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance.
* Researchers have made efforts to exclude unethical content from the training data — such as profanity, slurs, bias, and discriminatory language. However, despite these efforts, the model may still produce inappropriate expressions or factual inaccuracies.
### License
Mi:dm 2.0 is licensed under the [MIT License](./LICENSE).
<!-- ### Citation
```
@misc{,
title={},
author={},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={},
}
``` -->
### Contact
Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com
<br>