mykor / Midm-2.0-Base-Instruct-128K

Mi:dm 2.0 Base

🤗 Mi:dm 2.0 Models | 📜 Mi:dm 2.0 Technical Report | 📕 Mi:dm 2.0 Technical Blog*

_{*To be released soon}

News 📢

• 🔜 (Coming Soon!) GGUF format model files will be available soon for easier local deployment.
• ⚡️ 2025/07/04 : Released Mi:dm 2.0 Model collection on Hugging Face🤗.
  
  

Table of Contents

• Overview
  • Mi:dm 2.0
  • Quickstart
  • Evaluation
• Usage
  • Run on Friendli.AI
  • Run on Your Local Machine
  • Deployment
  • Tutorials
• More Information
  • Limitation
  • License
  • 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 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.

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:

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]))

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 .

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

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

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: [email protected]