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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/MiniMax-M2.7-GGUF",
	filename="MiniMaxAI-MiniMax-M2.7-IQ3_XXS.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 batiai/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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf batiai/MiniMax-M2.7-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/MiniMax-M2.7-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf batiai/MiniMax-M2.7-GGUF:IQ3_XXS

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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf batiai/MiniMax-M2.7-GGUF:IQ3_XXS

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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/MiniMax-M2.7-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/batiai/MiniMax-M2.7-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/batiai/MiniMax-M2.7-GGUF:IQ3_XXS

Ollama
How to use batiai/MiniMax-M2.7-GGUF with Ollama:
```
ollama run hf.co/batiai/MiniMax-M2.7-GGUF:IQ3_XXS
```

Unsloth Studio new

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

Pi new

How to use batiai/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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS

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": "batiai/MiniMax-M2.7-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS

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 batiai/MiniMax-M2.7-GGUF:IQ3_XXS

Run Hermes

hermes

Docker Model Runner
How to use batiai/MiniMax-M2.7-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/MiniMax-M2.7-GGUF:IQ3_XXS
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/MiniMax-M2.7-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.MiniMax-M2.7-GGUF-IQ3_XXS

List all available models

lemonade list

MiniMax M2.7 GGUF — Quantized by BatiAI

IQ3_XXS quantization of MiniMaxAI/MiniMax-M2.7 (229B Dense) for on-device AI on Mac. Built and verified by BatiAI for BatiFlow.

Why MiniMax M2.7?

229B Dense — one of the largest open models
Outperforms GPT-5.3 on GDPval-AA (ELO 1495)
Toolathon: 46.3% accuracy (global top tier)
Agent Teams, complex Skills, dynamic tool search
Runs on a 128GB MacBook Pro — no cloud needed

Quick Start

ollama pull batiai/minimax-m2.7:iq3

Available Quantizations

Quant	Size	VRAM	M4 Max (128GB)	Recommended For
IQ3_XXS	82GB	104GB	36.7 t/s	128GB+ Mac

Benchmarks — MacBook Pro M4 Max (128GB)

Metric	IQ3_XXS
Token gen (short)	22.1 t/s
Token gen (long, 300 tokens)	36.7 t/s
Prompt eval	14.8 t/s
VRAM	104 GB (97% GPU / 3% CPU)
Cold start	42 seconds
Korean output	✅
Tool call JSON	✅
Basic math (2+2)	✅

RAM Requirements

Your Mac RAM	IQ3_XXS (82GB)
64GB or less	❌ Won't fit
96GB	⚠️ Heavy swap, unusable
128GB	✅ 36.7 t/s
192GB+	✅ Fast, with headroom

229B on a Laptop

This is a 229B parameter dense model running entirely on-device — no cloud, no API, no costs. IQ3_XXS quantization compresses from 457GB (BF16) to 82GB while maintaining Korean, tool calling, and reasoning capabilities.

Model Comparison — Which BatiAI Model for Your Mac?

Your Mac	Best Model	Speed
16GB	`batiai/gemma4-e4b:q4`	57 t/s
24GB	`batiai/gemma4-26b:iq4`	85 t/s
36GB	`batiai/qwen3.5-35b:iq4`	26.6 t/s
48GB	`batiai/gemma4-31b:iq4`	22.8 t/s
128GB	`batiai/minimax-m2.7:iq3`	36.7 t/s

Why BatiAI Quantization?

	BatiAI	Third-party (unsloth, etc.)
Source	Quantized from official MiniMax weights	Re-quantized from other GGUFs
Tested on	Real MacBook Pro M4 Max (128GB)	Often untested on consumer hardware
Tool Calling	✅ Verified	Often untested
Korean	✅ Validated	Not tested
imatrix	✅ Calibrated for quality	Standard or none

Technical Details

Original Model: MiniMaxAI/MiniMax-M2.7
Architecture: Dense (229B params, all active)
License: Modified-MIT
Quantized with: llama.cpp
Quantized by: BatiAI

About BatiFlow

BatiFlow — free, on-device AI automation for Mac. 5MB app, 100% local, unlimited. 57+ built-in tools for calendar, notes, reminders, files, email, browser, messaging.

License

Quantized from MiniMaxAI/MiniMax-M2.7. License: Modified-MIT.

Downloads last month: 198

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

3-bit

4-bit

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

Base model

MiniMaxAI/MiniMax-M2.7

Quantized

(106)

this model

Collection including batiai/MiniMax-M2.7-GGUF

🚀 Frontier MoE — 128B–1T

Collection

Largest open-weight LLMs, BatiAI-quantized. Mac-runnable from M4 Max 128GB to Mac Studio M3 Ultra 512GB. • 6 items • Updated 1 day ago