Instructions to use cturan/MiniMax-M2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cturan/MiniMax-M2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="cturan/MiniMax-M2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cturan/MiniMax-M2-GGUF", dtype="auto")

llama-cpp-python

How to use cturan/MiniMax-M2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cturan/MiniMax-M2-GGUF",
	filename="minimax-m2-Q2_K.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 cturan/MiniMax-M2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cturan/MiniMax-M2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf cturan/MiniMax-M2-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cturan/MiniMax-M2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf cturan/MiniMax-M2-GGUF:Q2_K

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 cturan/MiniMax-M2-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf cturan/MiniMax-M2-GGUF:Q2_K

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 cturan/MiniMax-M2-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cturan/MiniMax-M2-GGUF:Q2_K

Use Docker

docker model run hf.co/cturan/MiniMax-M2-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use cturan/MiniMax-M2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cturan/MiniMax-M2-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": "cturan/MiniMax-M2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cturan/MiniMax-M2-GGUF:Q2_K

SGLang

How to use cturan/MiniMax-M2-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 "cturan/MiniMax-M2-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": "cturan/MiniMax-M2-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 "cturan/MiniMax-M2-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": "cturan/MiniMax-M2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use cturan/MiniMax-M2-GGUF with Ollama:
```
ollama run hf.co/cturan/MiniMax-M2-GGUF:Q2_K
```

Unsloth Studio new

How to use cturan/MiniMax-M2-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 cturan/MiniMax-M2-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 cturan/MiniMax-M2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cturan/MiniMax-M2-GGUF to start chatting

Pi new

How to use cturan/MiniMax-M2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cturan/MiniMax-M2-GGUF:Q2_K

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": "cturan/MiniMax-M2-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use cturan/MiniMax-M2-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 cturan/MiniMax-M2-GGUF:Q2_K

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 cturan/MiniMax-M2-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use cturan/MiniMax-M2-GGUF with Docker Model Runner:
```
docker model run hf.co/cturan/MiniMax-M2-GGUF:Q2_K
```

Lemonade

How to use cturan/MiniMax-M2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cturan/MiniMax-M2-GGUF:Q2_K

Run and chat with the model

lemonade run user.MiniMax-M2-GGUF-Q2_K

List all available models

lemonade list

Building and Running the Experimental `minimax` Branch of `llama.cpp`

Note:
This setup is experimental. The minimax branch will not work with the standard llama.cpp. Use it only for testing GGUF models with experimental features.

System Requirements (you can use any supported this is for ubuntu build commands)

Ubuntu 22.04
NVIDIA GPU with CUDA support
CUDA Toolkit 12.8 or later
CMake

Installation Steps

1. Install CUDA Toolkit 12.8

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-8

2. Set Environment Variables

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
export PATH=$PATH:$CUDA_HOME/bin

3. Install Build Tools

sudo apt install cmake

4. Clone the Experimental Branch

git clone --branch minimax --single-branch https://github.com/cturan/llama.cpp.git
cd llama.cpp

5. Build the Project

mkdir build
cd build
cmake .. -DLLAMA_CUDA=ON -DLLAMA_CURL=OFF
cmake --build . --config Release --parallel $(nproc --all)

Build Output

After the build is complete, the binaries will be located in:

llama.cpp/build/bin

Running the Model

Example command:

./llama-server -m minimax-m2-Q4_K.gguf -ngl 999 --cpu-moe --jinja -fa on -c 32000 --reasoning-format auto

This configuration offloads the experts to the CPU, so approximately 16 GB of VRAM is sufficient.

Notes

--cpu-moe enables CPU offloading for mixture-of-experts layers.
--jinja activates the Jinja templating engine.
Adjust -c (context length) and -ngl (GPU layers) according to your hardware.
Ensure the model file (minimax-m2-Q4_K.gguf) is available in the working directory.

All steps complete. The experimental CUDA-enabled build of llama.cpp is ready to use.

Downloads last month: 98

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

2-bit

6-bit

8-bit

Model tree for cturan/MiniMax-M2-GGUF

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MiniMaxAI/MiniMax-M2

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this model

Building and Running the Experimental minimax Branch of llama.cpp