--- license: mit language: - en - ko tags: - KT - K-intelligence - Mi:dm inference: true pipeline_tag: text-generation library_name: transformers ---


Mi:dm 2.0 Base

๐Ÿค— Mi:dm 2.0 Models | ๐Ÿ“œ Mi:dm 2.0 Technical Report | ๐Ÿ“• Mi:dm 2.0 Technical Blog*

*To be released soon


# News ๐Ÿ“ข - ๐Ÿ”œ _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._ - โšก๏ธ`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face๐Ÿค—.

# 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)

# 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 textโ€”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.
## 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 = "KT์— ๋Œ€ํ•ด ์†Œ๊ฐœํ•ด์ค˜" # message for inference messages = [ {"role": "system", "content": "Mi:dm(๋ฏฟ:์Œ)์€ KT์—์„œ ๊ฐœ๋ฐœํ•œ AI ๊ธฐ๋ฐ˜ ์–ด์‹œ์Šคํ„ดํŠธ์ด๋‹ค."}, {"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.
## Evaluation ### Korean
Model Society & Culture General Knowledge Instruction Following
K-Refer* K-Refer-Hard* Ko-Sovereign* HAERAE Avg. KMMLU Ko-Sovereign* Avg. Ko-IFEval Ko-MTBench Avg.
Qwen3-4B 53.6 42.9 35.8 50.6 45.7 50.6 42.5 46.5 75.9 63.0 69.4
Exaone-3.5-2.4B-inst 64.0 67.1 44.4 61.3 59.2 43.5 42.4 43.0 65.4 74.0 68.9
Mi:dm 2.0-Mini-inst 66.4 61.4 36.7 70.8 58.8 45.1 42.4 43.8 73.3 74.0 73.6
Qwen3-14B 72.4 65.7 49.8 68.4 64.1 55.4 54.7 55.1 83.6 71 77.3
Llama-3.1-8B-inst 43.2 36.4 33.8 49.5 40.7 33.0 36.7 34.8 60.1 57 58.5
Exaone-3.5-7.8B-inst 71.6 69.3 46.9 72.9 65.2 52.6 45.6 49.1 69.1 79.6 74.4
Mi:dm 2.0-Base-inst 89.6 86.4 56.3 81.5 78.4 57.3 58.0 57.7 82 89.7 85.9
Model Comprehension Reasoning
K-Prag* K-Refer-Hard* Ko-Best Ko-Sovereign* Avg. Ko-Winogrande Ko-Best LogicKor HRM8K Avg.
Qwen3-4B 73.9 56.7 91.5 43.5 66.6 67.5 69.2 5.6 56.7 43.8
Exaone-3.5-2.4B-inst 68.7 58.5 87.2 38.0 62.5 60.3 64.1 7.4 38.5 36.7
Mi:dm 2.0-Mini-inst 69.5 55.4 80.5 42.5 61.9 61.7 64.5 7.7 39.9 37.4
Qwen3-14B 86.7 74.0 93.9 52.0 76.8 77.2 75.4 6.4 64.5 48.8
Llama-3.1-8B-inst 59.9 48.6 77.4 31.5 51.5 40.1 26.0 2.4 30.9 19.8
Exaone-3.5-7.8B-inst 73.5 61.9 92.0 44.0 67.2 64.6 60.3 8.6 49.7 39.5
Mi:dm 2.0-Base-inst 86.5 70.8 95.2 53.0 76.1 75.1 73.0 8.6 52.9 44.8
`*` indicates KT proprietary evaluation resources.
### English
Model Instruction Reasoning Math Coding General Knowledge
IFEval BBH GPQA MuSR Avg. GSM8K MBPP+ MMLU-pro MMLU Avg.
Qwen3-4B 79.7 79.0 39.8 58.5 59.1 90.4 62.4 - 73.3 73.3
Exaone-3.5-2.4B-inst 81.1 46.4 28.1 49.7 41.4 82.5 59.8 - 59.5 59.5
Mi:dm 2.0-Mini-inst 73.6 44.5 26.6 51.7 40.9 83.1 60.9 - 56.5 56.5
 
Qwen3-14B 83.9 83.4 49.8 57.7 63.6 88.0 73.4 70.5 82.7 76.6
Llama-3.1-8B-inst 79.9 60.3 21.6 50.3 44.1 81.2 81.8 47.6 70.7 59.2
Exaone-3.5-7.8B-inst 83.6 50.1 33.1 51.2 44.8 81.1 79.4 40.7 69.0 54.8
Mi:dm 2.0-Base-inst 84.0 77.7 33.5 51.9 54.4 91.6 77.5 53.3 73.7 63.5

# 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.

Left Image Right Image

## 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-Base-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).


# 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 โ€” 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). ## Contact Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com