Instructions to use Goldkoron/MiniMax-M2.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Goldkoron/MiniMax-M2.7 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Goldkoron/MiniMax-M2.7", filename="MiniMax-M2.7-K_G_2.50.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Goldkoron/MiniMax-M2.7 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goldkoron/MiniMax-M2.7 # Run inference directly in the terminal: llama cli -hf Goldkoron/MiniMax-M2.7
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goldkoron/MiniMax-M2.7 # Run inference directly in the terminal: llama cli -hf Goldkoron/MiniMax-M2.7
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 Goldkoron/MiniMax-M2.7 # Run inference directly in the terminal: ./llama-cli -hf Goldkoron/MiniMax-M2.7
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 Goldkoron/MiniMax-M2.7 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Goldkoron/MiniMax-M2.7
Use Docker
docker model run hf.co/Goldkoron/MiniMax-M2.7
- LM Studio
- Jan
- Ollama
How to use Goldkoron/MiniMax-M2.7 with Ollama:
ollama run hf.co/Goldkoron/MiniMax-M2.7
- Unsloth Studio
How to use Goldkoron/MiniMax-M2.7 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 Goldkoron/MiniMax-M2.7 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 Goldkoron/MiniMax-M2.7 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Goldkoron/MiniMax-M2.7 to start chatting
- Pi
How to use Goldkoron/MiniMax-M2.7 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Goldkoron/MiniMax-M2.7
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": "Goldkoron/MiniMax-M2.7" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Goldkoron/MiniMax-M2.7 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Goldkoron/MiniMax-M2.7
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 Goldkoron/MiniMax-M2.7
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Goldkoron/MiniMax-M2.7 with Docker Model Runner:
docker model run hf.co/Goldkoron/MiniMax-M2.7
- Lemonade
How to use Goldkoron/MiniMax-M2.7 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Goldkoron/MiniMax-M2.7
Run and chat with the model
lemonade run user.MiniMax-M2.7-{{QUANT_TAG}}List all available models
lemonade list
The K_G_3.50 quant seems to be outperforming the UD IQ4_NL quant. Whats the difference between v1 and v2?
I see you uploaded a v2 whats the difference?
I see you uploaded a v2 whats the difference?
I fixed the one tensor mentioned in this post to always be Q6_K instead of Q4_K or Q5_K on the v2 quants - https://old.reddit.com/r/LocalLLaMA/comments/1slk4di/minimax_m27_gguf_investigation_fixes_benchmarks/
It might not be necessary as I didn't see NaN when I tested them, but it's just in case. I will be removing the old ones and renaming v2 for them.
will redownload
I see you uploaded a v2 whats the difference?
I fixed the one tensor mentioned in this post to always be Q6_K instead of Q4_K or Q5_K on the v2 quants - https://old.reddit.com/r/LocalLLaMA/comments/1slk4di/minimax_m27_gguf_investigation_fixes_benchmarks/
It might not be necessary as I didn't see NaN when I tested them, but it's just in case. I will be removing the old ones and renaming v2 for them.
v2 passes the vibe check it does feel smarter. Nice work this is both smaller than the UD IQ4_NL quant and smarter.
It gives alot more nuance in its answers compared to UD IQ4_NL. How did you quant it so good this shits good lol.
It gives alot more nuance in its answers compared to UD IQ4_NL. How did you quant it so good this shits good lol.
I am actually not a big fan of the IQ4_NL or IQ4_XS tensors since they gave me less interesting outputs in inference time
The quant types I consider useful are: IQ1_S, IQ2_XXS, IQ2_XS, IQ2_S, IQ3_XXS, Q3_K, Q4_K, Q5_K, Q6_K
My quants usually use at least 3 different quant levels distributed across the tensors depending on how important/unimportant they are. More important tensors are getting higher than average quant level compared to traditional quants while the less sensitive tensors are actually being downgraded more than usual to fit the bpw budget.
That said, Minimax did not seem to quant nearly as well as my Qwen 3.5 tunes if you compare the KLD levels. And the comparisons with Unsloth's UD quants on this one is not that big of a disparity in KLD.
This quant has felt much better than the UD IQ4_NL and UD Q3_K_XL quants I tested. IQ4_NL will spew more confident sounding BS and likes to format responses weirdly where as the 3.50 bpw quant is very solid.
yet again this quant still does much better than the iq4_nl. The iq4_nl kept searching the same typo it made over and over while the 3.5 bpw caught the typo and correctly completed the search.