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

Libraries

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF",
	filename="MiniMax-M2.7-abliterated-BF16/MiniMax-M2.7-abliterated-BF16-00001-of-00010.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Youssofal/MiniMax-M2.7-Abliterated-Heretic-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": "Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M

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

Unsloth Studio

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF to start chatting

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Youssofal/MiniMax-M2.7-Abliterated-Heretic-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": "Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-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 Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M
```

Lemonade

How to use Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Youssofal/MiniMax-M2.7-Abliterated-Heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M2.7-Abliterated-Heretic-GGUF-Q4_K_M

List all available models

lemonade list

MiniMax-M2.7-Abliterated-Heretic-GGUF / README.md

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metadata

base_model: MiniMaxAI/MiniMax-M2.7
library_name: gguf
pipeline_tag: text-generation
license: other
license_name: non-commercial
license_link: https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE
tags:
  - gguf
  - minimax
  - minimax_m2
  - moe
  - mixture-of-experts
  - abliterated
  - uncensored
  - heretic
  - ara
  - llama-cpp
quantized_by: Youssofal

MiniMax-M2.7-Abliterated-Heretic-GGUF

This is a GGUF release of an abliterated version of MiniMaxAI's MiniMax-M2.7.

By applying Heretic's Ablated Refusal Adaptation (ARA), the base refusal behavior was removed at the weight level. The result keeps MiniMax-M2.7's sparse MoE reasoning, long-context instruction following, and general capability profile, but no longer defaults to the original refusal pattern.

Methodology & Model Notes

MiniMax-M2.7 is a 229B sparse MoE model with 10B active parameters per token, 62 layers, hybrid attention, 256 local experts with 8 active per token, and a 200K context window.

This release was produced with a direct Heretic ARA run using the fixed parameter set below:

start_layer_index = 30
end_layer_index = 51
preserve_good_behavior_weight = 0.4512
steer_bad_behavior_weight = 0.0037
overcorrect_relative_weight = 0.8804
neighbor_count = 14

The direct ARA run completed with Refusals: 0/25.

The resulting abliterated checkpoint was exported to BF16 and then converted to GGUF for llama.cpp-compatible deployment.

Files

MiniMax-M2.7-abliterated-BF16/: BF16 GGUF split into 10 parts
MiniMax-M2.7-abliterated-Q8_0/: Q8_0 GGUF split into 5 parts
MiniMax-M2.7-abliterated-Q3_K_M/: Q3_K_M GGUF split for Hub delivery
Additional quants will be added from the same abliterated BF16 GGUF source

Prompt Format

]~!b[]~b]system
{system_prompt}[e~[
]~b]user
{prompt}[e~[
]~b]ai
<think>

Running

llama-server \
  -m <quant-file.gguf> \
  -ngl 999 -c 32768 --jinja \
  --reasoning-format auto -fa \
  --temp 1.0 --top-p 0.95 --top-k 40

Model Architecture

Spec	Value
Total Parameters	229B (sparse MoE)
Active Parameters	10B per token
Experts	256 local, 8 per token
Layers	62
Attention	Hybrid: 7 Lightning + 1 softmax per 8-block
Context	200K
Base Model	MiniMaxAI/MiniMax-M2.7

Disclaimer

This model has had refusal behavior removed at the weight level. It will answer prompts that the base model would normally refuse. You are responsible for how you use it.

Credits

Base model: MiniMaxAI/MiniMax-M2.7
Refusal removal pipeline: Heretic with the ARA method
GGUF runtime and quantization: llama.cpp

License

This release inherits the base MiniMax-M2.7 license.

NON-COMMERCIAL. Commercial use requires written authorization from MiniMax.