Instructions to use moxin-org/MiniMax-M2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use moxin-org/MiniMax-M2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="moxin-org/MiniMax-M2-GGUF", filename="Moxin-MXFP4_MOE/MiniMax-M2-MXFP4_MOE-00001-of-00006.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use moxin-org/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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL # Run inference directly in the terminal: llama-cli -hf moxin-org/MiniMax-M2-GGUF:Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf moxin-org/MiniMax-M2-GGUF:Q4_K_XL # Run inference directly in the terminal: llama-cli -hf moxin-org/MiniMax-M2-GGUF:Q4_K_XL
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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf moxin-org/MiniMax-M2-GGUF:Q4_K_XL
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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf moxin-org/MiniMax-M2-GGUF:Q4_K_XL
Use Docker
docker model run hf.co/moxin-org/MiniMax-M2-GGUF:Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use moxin-org/MiniMax-M2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "moxin-org/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": "moxin-org/MiniMax-M2-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/moxin-org/MiniMax-M2-GGUF:Q4_K_XL
- Ollama
How to use moxin-org/MiniMax-M2-GGUF with Ollama:
ollama run hf.co/moxin-org/MiniMax-M2-GGUF:Q4_K_XL
- Unsloth Studio
How to use moxin-org/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 moxin-org/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 moxin-org/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 moxin-org/MiniMax-M2-GGUF to start chatting
- Pi
How to use moxin-org/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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL
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": "moxin-org/MiniMax-M2-GGUF:Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use moxin-org/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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL
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 moxin-org/MiniMax-M2-GGUF:Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use moxin-org/MiniMax-M2-GGUF with Docker Model Runner:
docker model run hf.co/moxin-org/MiniMax-M2-GGUF:Q4_K_XL
- Lemonade
How to use moxin-org/MiniMax-M2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull moxin-org/MiniMax-M2-GGUF:Q4_K_XL
Run and chat with the model
lemonade run user.MiniMax-M2-GGUF-Q4_K_XL
List all available models
lemonade list
Why?
Why should I use yours vs unsloth?
Thanks a lot for the question!
As we wrote in the Acknowledgements, our work is deeply inspired by many open-source authors, and we see them as our heroes and pioneers rather than competitors. In particular, we are very grateful to the unsloth team: their BF16 GGUF releases and imatrix files made it much easier for others (including us) to imagine and share additional quantization variants.
Regarding our quantizations : we tend to keep higher precision for the attention part (since the experts are the really “chubby” part, lol), we also try to keep the quantization pattern as consistent as possible across layers. We believe these may be helpful for some edge-deployment scenarios and for people experimenting with customized accelerations.
In the end, which quant to use is entirely your choice. We are happy to answer any questions you have and equally happy if our work is just one more option on your shortlist. Thank you again for your interest and for taking the time to compare different projects!