Instructions to use Mungert/Midm-2.0-Base-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mungert/Midm-2.0-Base-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mungert/Midm-2.0-Base-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mungert/Midm-2.0-Base-Instruct-GGUF",
	filename="Midm-2.0-Base-Instruct-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mungert/Midm-2.0-Base-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/Midm-2.0-Base-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

SGLang

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mungert/Midm-2.0-Base-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/Midm-2.0-Base-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mungert/Midm-2.0-Base-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/Midm-2.0-Base-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Ollama:
```
ollama run hf.co/Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/Midm-2.0-Base-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/Midm-2.0-Base-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/Midm-2.0-Base-Instruct-GGUF to start chatting

Pi new

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use Mungert/Midm-2.0-Base-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/Midm-2.0-Base-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Midm-2.0-Base-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Midm-2.0-Base-Instruct GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 21c02174.

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

Click here to get info on choosing the right GGUF model format

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🤗.

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.

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
Model	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
Model	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
Model	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.

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 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: midm-llm@kt.com

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

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GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

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Midm-2.0-Base-Instruct-GGUF