Instructions to use dennny123/MiniMax-M2.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dennny123/MiniMax-M2.7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dennny123/MiniMax-M2.7-GGUF",
	filename="MiniMax-M2.7-BF16.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 dennny123/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 dennny123/MiniMax-M2.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dennny123/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 dennny123/MiniMax-M2.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dennny123/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 dennny123/MiniMax-M2.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dennny123/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 dennny123/MiniMax-M2.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dennny123/MiniMax-M2.7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use dennny123/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 "dennny123/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": "dennny123/MiniMax-M2.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M

Ollama
How to use dennny123/MiniMax-M2.7-GGUF with Ollama:
```
ollama run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M
```

Unsloth Studio new

How to use dennny123/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 dennny123/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 dennny123/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 dennny123/MiniMax-M2.7-GGUF to start chatting

Pi new

How to use dennny123/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 dennny123/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": "dennny123/MiniMax-M2.7-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dennny123/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 dennny123/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 dennny123/MiniMax-M2.7-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use dennny123/MiniMax-M2.7-GGUF with Docker Model Runner:
```
docker model run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M
```

Lemonade

How to use dennny123/MiniMax-M2.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dennny123/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

🔥 GGUF Quantizations of MiniMax-M2.7

Quantized using llama.cpp from BF16 source weights.

Original model: MiniMaxAI/MiniMax-M2.7

Run them in LM Studio or directly with llama.cpp.

Download a file from below

Filename	Quant type	File Size	Description
MiniMax-M2.7-BF16.gguf	BF16	~427 GB	Full BF16 weights. Use for re-quantizing or max quality.
MiniMax-M2.7-Q8_0.gguf	Q8_0	~243 GB	Extremely high quality, generally unneeded but max available.
MiniMax-M2.7-Q6_K.gguf	Q6_K	~188 GB	Very high quality, near perfect, recommended.
MiniMax-M2.7-Q5_K_M.gguf	Q5_K_M	~162 GB	High quality, recommended.
MiniMax-M2.7-Q4_K_M.gguf	Q4_K_M	~138 GB	Good quality, default size for most use cases, recommended.
MiniMax-M2.7-Q3_K_M.gguf	Q3_K_M	~109 GB	Lower quality but usable, good for tight hardware.
MiniMax-M2.7-Q2_K.gguf	Q2_K	~83 GB	Low quality, only for extreme memory constraints.

Downloading

pip install -U "huggingface_hub[cli]"

huggingface-cli download dennny123/MiniMax-M2.7-GGUF --include "MiniMax-M2.7-Q4_K_M*" --local-dir ./

For split files (>50GB):

huggingface-cli download dennny123/MiniMax-M2.7-GGUF --include "MiniMax-M2.7-Q8_0/*" --local-dir ./

Running the model

llama.cpp

./llama-cli -m MiniMax-M2.7-Q4_K_M.gguf -ngl 99 -cnv -p "You are a helpful assistant."

Ollama

ollama run hf.co/dennny123/MiniMax-M2.7-GGUF:Q4_K_M

LM Studio

Search for dennny123/MiniMax-M2.7-GGUF in the model browser.

Which file should I choose?

Have this much memory	Use this quant
256GB+	Q8_0 or Q6_K
192GB	Q5_K_M
144GB	Q4_K_M (most popular)
112GB	Q3_K_M
96GB	Q2_K

MiniMax-M2.7 is a Mixture-of-Experts model (229B total, ~10B active per token). All 229B parameters must be loaded into memory even though only a fraction are active per token. Size your hardware by total parameter count.

Quantization details

llama.cpp: Latest main branch
Conversion: BF16 GGUF intermediate, quantized in second pass
Hardware: NVIDIA GH200 96GB + 525GB RAM

About MiniMax-M2.7

MiniMax-M2.7 is a 229B parameter MoE model (10B active) built for coding and agentic workflows.

SWE-Pro: 56.22% (matches GPT-5.3-Codex)
VIBE-Pro: 55.6%
Terminal Bench 2: 57.0%
GDPval-AA: ELO 1495 (highest open-source, surpasses GPT-5.3)
MLE Bench Lite: 66.6% medal rate

Recommended inference parameters: temperature=1.0, top_p=0.95, top_k=40

See the official model card for full details.

Downloads last month: 547

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for dennny123/MiniMax-M2.7-GGUF

Base model

MiniMaxAI/MiniMax-M2.7

Quantized

(107)

this model