🔧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🤗.

# Table of Contents

Overview
Usage
More Information

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.

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.

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.

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 for more information

Deployment

Basic Serving

To serve Mi:dm 2.0 using vLLM(>=0.8.0) with an OpenAI-compatible API:

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:

Download and place Mi:dm 2.0 parser file in your working directory.
Run the following Docker command to launch the vLLM server with our custom parser file:
bash
1docker run --rm -it --gpus all -p 8000:8000 \ 2 -e HUGGING_FACE_HUB_TOKEN="<YOUR_HUGGINGFACE_TOKEN>" \ 3 -v "$(pwd)/midm_parser.py:/custom/midm_parser.py" \ 4 vllm/vllm-openai:v0.11.0 \ 5 --model K-intelligence/Midm-2.0-Base-Instruct \ 6 --enable-auto-tool-choice \ 7 --tool-parser-plugin /custom/midm_parser.py \ 8 --tool-call-parser midm-parser \ 9 --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.

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.

Contact

Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com