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Midm-2.0-Mini-Instruct/README.md
ModelHub XC 9178baa65a 初始化项目，由ModelHub XC社区提供模型 
Model: K-intelligence/Midm-2.0-Mini-Instruct
Source: Original Platform
2026-05-07 00:13:36 +08:00

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---
license: mit
language:
- en
- ko
tags:
- KT
- K-intelligence
- Mi:dm
pipeline_tag: text-generation
library_name: transformers
---
<p align="center">
<br>
<span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0 Mini</span>
</br>
</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> |
📕 <a href="https://kode.kt.com/blog/article/3935">Mi:dm 2.0 Technical Blog</a>
</p>
<p align="center"><sub>*To be released soon</sub></p>
<br>
# News 📢
- 🔧`2025/10/29`: Added support for function calling on vLLM with Mi:dm 2.0 parser.
- 📕`2025/08/08`: Published a technical blog article about Mi:dm 2.0 Model.
- ⚡️`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
#### Basic Serving
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
```
#### With Function Calling
For advanced function calling tasks, you can serve Mi:dm 2.0 with our own tool parser:
1. Download and place [Mi:dm 2.0 parser file](https://github.com/K-intelligence-Midm/Midm-2.0/blob/main/tutorial/03_open-webui/modelfile/midm_parser.py) in your working directory.
2. Run the following Docker command to launch the vLLM server with our custom parser file:
```bash
docker run --rm -it --gpus all -p 8000:8000 \
-e HUGGING_FACE_HUB_TOKEN="<YOUR_HUGGINGFACE_TOKEN>" \
-v "$(pwd)/midm_parser.py:/custom/midm_parser.py" \
vllm/vllm-openai:v0.11.0 \
--model K-intelligence/Midm-2.0-Mini-Instruct \
--enable-auto-tool-choice \
--tool-parser-plugin /custom/midm_parser.py \
--tool-call-parser midm-parser \
--host 0.0.0.0
```
>[!Note]
> This setup is compatible with `vllm/vllm-openai:v0.8.0` and later, but we strongly recommend using `v0.11.0` for optimal stability and compatibility with our parser.
## 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>
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