Instructions to use Goldkoron/MiniMax-M2.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

llama.cpp

How to use Goldkoron/MiniMax-M2.7 with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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 new

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-server -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-server -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

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

MiniMax-M2.7 / README.md

Goldkoron

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metadata

license: other
license_name: modified-mit
license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
base_model: MiniMaxAI/MiniMax-M2.7
tags:
  - gguf
  - moe
  - quantized
  - minimax

MiniMax-M2.7 — Gutenberg Quants

Quantizations of MiniMax-M2.7 using the Gutenberg (K_G) quantization strategy.

Available Quants

Quant	Size	BPW	Mean KLD	Same Top P
K_G_5.00	133.1 GiB	5.00	0.022412	92.447%
K_G_4.50	119.7 GiB	4.50	0.029416	91.311%
K_G_4.00	106.4 GiB	4.00	0.044050	89.497%
K_G_3.50	93.1 GiB	3.50	0.061226	87.641%
K_G_3.00	79.9 GiB	3.00	0.098738	84.454%
K_G_2.50	66.6 GiB	2.50	0.172875	80.034%

KLD and Same Top P measured against Q6_K expert reference logits (8192 context, 10 chunks).

vs Standard Quants (unsloth)

Gutenberg	BPW	KLD	Standard (unsloth)	BPW	KLD
K_G_2.50	2.50	0.172875	UD-IQ2_M	2.45	0.191059
K_G_3.00	3.00	0.098738	UD-IQ3_XXS	2.80	0.119762
K_G_3.50	3.50	0.061226	UD-Q3_K_M	3.54	0.063647
K_G_4.00	4.00	0.044050	UD-IQ4_XS	3.79	0.051081
K_G_5.00	5.00	0.022412	UD-Q4_K_M	4.90	0.024529

Why Gutenberg?

Standard quantization applies uniform rules to all tensors. Gutenberg uses KLD sensitivity data to allocate precision where it matters most, upgrading the tensors that have the highest measured impact on output quality while keeping less important tensors at the base level.

The result is significantly better quality than standard quants at the same model size.

Compatibility

Fully compatible with stock llama.cpp, llama-server, LM Studio, and any GGUF-compatible runtime. No custom builds required.