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": "MiniMax-M2.7-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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 / README.md

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	---
	language:
	- en
	- ko
	- ja
	- zh
	license: other
	license_name: modified-mit
	license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
	tags:
	- gguf
	- minimax
	- quantized
	- apple-silicon
	- ollama
	- batiai
	- on-device
	- 229b
	base_model: MiniMaxAI/MiniMax-M2.7
	pipeline_tag: text-generation
	library_name: llama.cpp
	---

	# MiniMax M2.7 GGUF — Quantized by BatiAI

	<p align="center">
	<a href="https://flow.bati.ai"><img src="https://img.shields.io/badge/BatiFlow-macOS%20AI%20Automation-blue?style=for-the-badge&logo=apple" alt="BatiFlow"></a>
	<a href="https://ollama.com/batiai/minimax-m2.7"><img src="https://img.shields.io/badge/Ollama-batiai%2Fminimax--m2.7-green?style=for-the-badge" alt="Ollama"></a>
	</p>

	> IQ3_XXS quantization of MiniMaxAI/MiniMax-M2.7 (229B Dense) for on-device AI on Mac.
	> Built and verified by [BatiAI](https://bati.ai) for [BatiFlow](https://flow.bati.ai).

	## 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

	```bash
	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](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) \| 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](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)
	- Architecture: Dense (229B params, all active)
	- License: Modified-MIT
	- Quantized with: [llama.cpp](https://github.com/ggml-org/llama.cpp)
	- Quantized by: [BatiAI](https://bati.ai)

	## About BatiFlow

	[BatiFlow](https://flow.bati.ai) — 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](https://huggingface.co/MiniMaxAI/MiniMax-M2.7). License: Modified-MIT.