Instructions to use unsloth/MiniMax-M2.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use unsloth/MiniMax-M2.7-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/MiniMax-M2.7-GGUF", filename="BF16/MiniMax-M2.7-BF16-00001-of-00010.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 unsloth/MiniMax-M2.7-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
- LM Studio
- Jan
- vLLM
How to use unsloth/MiniMax-M2.7-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/MiniMax-M2.7-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": "unsloth/MiniMax-M2.7-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
- Ollama
How to use unsloth/MiniMax-M2.7-GGUF with Ollama:
ollama run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
- Unsloth Studio new
How to use unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/MiniMax-M2.7-GGUF to start chatting
- Pi new
How to use unsloth/MiniMax-M2.7-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf unsloth/MiniMax-M2.7-GGUF:UD-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": "unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/MiniMax-M2.7-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 unsloth/MiniMax-M2.7-GGUF:UD-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 unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use unsloth/MiniMax-M2.7-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
- Lemonade
How to use unsloth/MiniMax-M2.7-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/MiniMax-M2.7-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.MiniMax-M2.7-GGUF-UD-Q4_K_M
List all available models
lemonade list
larger file size for same quant
q4_k_xl for m2.5 is 131gb, while m2.7 is 141gb
Oh the old Q4_K_XL is different :) Our new method guarantees always _XL is always bigger than _M.
I would use Q4_K_M which is also dynamic!
@shimmyshimmer thank you for all the quick work you are always doing.
It would be great if you could write-up at unsloth.ai what all the suffixes mean, and how to pick a quant, especially since you've updated your methods. Also, apologies if that is written somewhere and I've missed it.
I've always just picked the XL, assuming bigger is better, and hoping the accuracy is better.
How come you are recommending Q4_K_M in this case? π
Oh no no _XL is always better yes - but it's always now guaranteed to be larger.
_K_XL >= _K_L >= _K_M >= _K_S >= _K_XS
Oh no no _XL is always better yes - but it's always now guaranteed to be larger.
_K_XL >= _K_L >= _K_M >= _K_S >= _K_XS
i suppose it's because q4_k_m is only 1gb smaller than q4_k_xl, so it's not really worth it
Oh the old Q4_K_XL is different :) Our new method guarantees always _XL is always bigger than _M.
I would use Q4_K_M which is also dynamic!
Could you please explain exactly why?
Is this some kind of personal preference regarding the quality/filesize ratio?
Or is there a technical, sensible reason that would make this a recommendation for everyone?
I mean, are there any disadvantages if I choose the _K_XL version, assuming that I have enough RAM anyway and I am happy with the speed?