Instructions to use AesSedai/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 AesSedai/MiniMax-M2.7-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AesSedai/MiniMax-M2.7-GGUF", filename="IQ3_S/MiniMax-M2.7-IQ3_S-00001-of-00003.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use AesSedai/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 AesSedai/MiniMax-M2.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/MiniMax-M2.7-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 AesSedai/MiniMax-M2.7-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/MiniMax-M2.7-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 AesSedai/MiniMax-M2.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/MiniMax-M2.7-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 AesSedai/MiniMax-M2.7-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/MiniMax-M2.7-GGUF with Ollama:
ollama run hf.co/AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
- Unsloth Studio new
How to use AesSedai/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 AesSedai/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 AesSedai/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 AesSedai/MiniMax-M2.7-GGUF to start chatting
- Pi new
How to use AesSedai/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 AesSedai/MiniMax-M2.7-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": "AesSedai/MiniMax-M2.7-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AesSedai/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 AesSedai/MiniMax-M2.7-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 AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use AesSedai/MiniMax-M2.7-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/MiniMax-M2.7-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/MiniMax-M2.7-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MiniMax-M2.7-GGUF-Q4_K_M
List all available models
lemonade list
How does IQ4_XS compare to UD-Q3_K_XL?
Does it have a noticeable intelligence increase?
Maybe it's just me but I often have bad luck with unsloth's quants. Anyway I found this benchmark (no KLD or PPL, just "accuracy") https://unsloth.ai/docs/models/minimax-m27#gguf-benchmarks
I haven't had time to play with this model outside of quanting and PPL / KLD testing it to make sure it's sane, but given that the quant recipes I use keep the majority of the model in Q8_0 with only the ffn_gate / ffn_up / ffn_down tensors being quantized down, I'd suggest that mine are probably a little better on KLD and probably do better at long context since I keep the attention and other non-expert tensors in high quality.
You'd have to do a downstream bench to verify though, I might add some of that to my testing suite in the future in addition to just KLD but for now you'll have to test them side-by-side. I'm sure other people will also be interested to know!
