Instructions to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF",
	filename="gemma-4-26B-A4B-it-UD-IQ2_M.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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-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": "groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-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/groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

Ollama
How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with Ollama:
```
ollama run hf.co/groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
```

Unsloth Studio

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-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": "groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-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 groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M
```

Lemonade

How to use groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.Huihui-gemma-4-26B-A4B-it-abliterated-GGUF-UD-Q4_K_M

List all available models

lemonade list

Huihui Gemma 4 26B A4B GGUF banner

Huihui Gemma 4 26B A4B IT Abliterated — GGUF Quantizations

This repository contains GGUF / llama.cpp quantized builds of:

huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated

These are UD quantizations prepared for efficient local inference with llama.cpp, including support for multimodal image-text-to-text workflows when used with the corresponding mmproj file.

Overview

This release is designed for users who want to run the Huihui Gemma 4 26B A4B abliterated model locally with reduced VRAM and RAM requirements while preserving as much output quality as possible.

The quantization variants use an optimized tensor distribution strategy inspired by Unsloth-style mixed-quality quantization recipes, balancing model fidelity, speed, and memory efficiency across different hardware targets.

Quick Start

Download the latest release of llama.cpp.
Download your preferred .gguf model file from this repository.
For multimodal inference, also download the matching mmproj file.
Run the model with llama.cpp using your preferred frontend or CLI.

Example:

./llama-cli \
  -m Huihui-Gemma-4-26B-A4B-it-abliterated-UD-Q4_K_XL.gguf \
  --mmproj mmproj-model.gguf \
  -p "Describe this image in detail."

Adjust the model filename and mmproj filename to match the files you downloaded.

Which Quant Should I Choose?

Choose based on your available memory and quality target:

Higher-bit / larger quants: Better quality, higher VRAM/RAM usage.
Mid-range quants: Best balance for most local setups.
Lower-bit quants: Faster and smaller, but with more quality loss.

For best results, use the largest quantization your hardware can comfortably run.

Multimodal Usage

This model supports image-text-to-text inference when used with the appropriate multimodal projection file.

Make sure the mmproj file matches this model family. Using an incorrect projection file may result in broken or degraded vision-language behavior.

Notes

This is a quantized GGUF release of the fine-tuned model.
Original model: huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated
Runtime target: llama.cpp
Format: GGUF
Modality: image-text-to-text
Quantization style: UD / mixed tensor distribution

Disclaimer

This repository only provides quantized GGUF builds. Model behavior, alignment characteristics, and training details are inherited from the original base model and fine-tune.

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GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for groxaxo/Huihui-gemma-4-26B-A4B-it-abliterated-GGUF

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated

Quantized

(12)

this model