| | --- |
| | 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"> </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: [email protected] |
| |
|
| | <br> |
