| --- |
| license: mit |
| language: |
| - en |
| - ko |
| tags: |
| - KT |
| - K-intelligence |
| - Mi:dm |
| inference: true |
| pipeline_tag: text-generation |
| library_name: transformers |
| --- |
| |
|
|
| <p align="center"> |
| <br> |
| <span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0 Base</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> |
|
|
|
|
| <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 Friendli.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-Base-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> |
|
|
| ## 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"> </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-Base-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-Base-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). |
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| # More Information |
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| ## 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. |
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| * The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance. |
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| * 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. |
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| ## License |
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| Mi:dm 2.0 is licensed under the [MIT License](./LICENSE). |
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| <!-- ### Citation |
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| ``` |
| @misc{, |
| title={}, |
| author={}, |
| year={2025}, |
| eprint={}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={}, |
| } |
| ``` --> |
| ## Contact |
| Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com |
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