Instructions to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF",
	filename="gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-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": "noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Ollama
How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with Ollama:
```
ollama run hf.co/noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-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 noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-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 noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF to start chatting

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

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": "noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull noctrex/gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

gemma-4-26B-A4B-it-uncensored-heretic-MXFP4_MOE-GGUF / README.md

noctrex

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metadata

pipeline_tag: image-text-to-text
base_model:
  - llmfan46/gemma-4-26B-A4B-it-uncensored-heretic

These are MXFP4 quantizations of the model gemma-4-26B-A4B-it-uncensored-heretic

Quick Start

Download the latest release of llama.cpp.
Download your preferred model variant from below.
For the mmproj file, it is recommended to use the F32 version for the best visual processing results. F32 > BF16 > F16

Which version should I choose?

All variants use MXFP4 for the MoE (Mixture of Experts) weights to keep the model efficient. The difference lies in how the remaining tensors are handled:

Variant	Quality	Performance	Size	Recommendation
BF16	⭐⭐⭐	Variable*	15.80GiB	Best for maximum accuracy; original unquantized weights.
F16	⭐⭐	Fast	15.80GiB	Great alternative if BF16 is slow on your hardware.
Q8	⭐	Fastest	14.36GiB	Balanced performance and memory usage.

Note: On some older architectures, BF16 may be slower than F16.
Check that your GPU supports native BF16

Read the guide from unsloth in order to set up the model's recommended settings:

The official chat template has been updated from Google. If you do not want to download the model again, you can just tell llama.cpp to use the new chat template.