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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tarruda/MiniMax-M2.7-GGUF",
	filename="Q4_K/MiniMax-M2.7-256x4.9B-Q4_K-00001-of-00004.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 tarruda/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 tarruda/MiniMax-M2.7-GGUF
# Run inference directly in the terminal:
llama-cli -hf tarruda/MiniMax-M2.7-GGUF

Install from WinGet (Windows)

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

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

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

Use Docker

docker model run hf.co/tarruda/MiniMax-M2.7-GGUF

LM Studio
Jan
Ollama
How to use tarruda/MiniMax-M2.7-GGUF with Ollama:
```
ollama run hf.co/tarruda/MiniMax-M2.7-GGUF
```

Unsloth Studio new

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

Pi new

How to use tarruda/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 tarruda/MiniMax-M2.7-GGUF

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tarruda/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 tarruda/MiniMax-M2.7-GGUF

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 tarruda/MiniMax-M2.7-GGUF

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.MiniMax-M2.7-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

MiniMax-M2.7-GGUF / scripts /generate-imatrix.sh

tarruda

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	#!/usr/bin/env bash

	set -euo pipefail

	# Validate that exactly 2 arguments are provided
	if [ $# -ne 2 ]; then
	echo "Error: Exactly 2 arguments required."
	echo "Usage: $0 <llama_cpp_dir> <gguf>"
	echo "Example: $0 ~/code/llama.cpp BF16/Qwen3.5-35B-A3B-BF16-00001-of-00003.gguf"
	exit 1
	fi

	# Assign arguments to variables for clarity
	LLAMA_CPP_DIR="$1"
	GGUF_PATH="$2"

	# Validate that the llama.cpp directory exists
	if [ ! -d "$LLAMA_CPP_DIR" ]; then
	echo "Error: llama.cpp directory not found: $LLAMA_CPP_DIR"
	exit 1
	fi


	# Validate that the Python script exists
	if [ ! -e "$GGUF_PATH" ]; then
	echo "Error: GGUF not found: $GGUF_PATH"
	exit 1
	fi

	CALIBRATION_DATASET_PATH=$HOME/calibration-data.txt

	if [ ! -e $CALIBRATION_DATASET_PATH ]; then
	# download the calibration dataset
	curl -L https://gist.githubusercontent.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a/raw/ba5b01b6960a86874592f5913e283746ff734483/ubergarm-imatrix-calibration-corpus-v02.txt > $CALIBRATION_DATASET_PATH
	fi

	# Get the directory where the script is located
	SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
	IMATRIX_OUT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
	IMATRIX_PATH="$IMATRIX_OUT_DIR/imatrix.gguf"

	if [ -e "$IMATRIX_PATH" ]; then
	echo "Error: imatrix already exists: $IMATRIX_PATH"
	exit 1
	fi

	$LLAMA_CPP_DIR/build/bin/llama-imatrix \
	-fit off \
	--model "$GGUF_PATH"\
	-f $CALIBRATION_DATASET_PATH \
	-o $IMATRIX_PATH \
	--ctx-size 512 \
	-ub 4096 -b 4096 \
	--no-mmap