Instructions to use marksverdhei/MiniMax-M2.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marksverdhei/MiniMax-M2.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="marksverdhei/MiniMax-M2.5-GGUF",
	filename="MiniMax-M2.5-IQ3_S.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 marksverdhei/MiniMax-M2.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use marksverdhei/MiniMax-M2.5-GGUF with Ollama:
```
ollama run hf.co/marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
```

Unsloth Studio new

How to use marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marksverdhei/MiniMax-M2.5-GGUF to start chatting

Pi new

How to use marksverdhei/MiniMax-M2.5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf marksverdhei/MiniMax-M2.5-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": "marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-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 marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use marksverdhei/MiniMax-M2.5-GGUF with Docker Model Runner:
```
docker model run hf.co/marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M
```

Lemonade

How to use marksverdhei/MiniMax-M2.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marksverdhei/MiniMax-M2.5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M2.5-GGUF-Q4_K_M

List all available models

lemonade list

MiniMax-M2.5 GGUF

GGUF quantizations of MiniMaxAI/MiniMax-M2.5, created with llama.cpp.

Model Details

Property	Value
Base model	MiniMaxAI/MiniMax-M2.5
Architecture	Mixture of Experts (MoE)
Total parameters	230B
Active parameters	10B per token
Layers	62
Total experts	256
Active experts per token	8
Source precision	FP8 (`float8_e4m3fn`)

Available Quantizations

Quantization	Size	Description
Q8_0	227 GB	8-bit quantization, highest quality
Q4_K_M	129 GB	4-bit K-quant (medium), good balance of quality and size
IQ3_S	92 GB	3-bit importance quantization (small), compact
Q2_K	78 GB	2-bit K-quant, smallest size

Usage

These GGUFs can be used with llama.cpp and compatible frontends.

# Example with llama-cli
llama-cli -m MiniMax-M2.5-Q4_K_M.gguf -p "Hello" -n 128

Notes

The source model uses FP8 (float8_e4m3fn) precision, so Q8_0 is effectively lossless relative to the source weights.
This is a large MoE model. Even the smallest quant (Q2_K) requires ~78GB due to the number of experts.
Quantized from the official MiniMaxAI/MiniMax-M2.5 weights.

Downloads last month: 42

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

2-bit

3-bit

4-bit

8-bit

Inference Providers NEW

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Model tree for marksverdhei/MiniMax-M2.5-GGUF

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