Instructions to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF",
	filename="Gemma-4-12B-it-AEON-Abliterated-Q3_K_M.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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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": "Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M

Ollama
How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with Ollama:
```
ollama run hf.co/Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open /spaces/unsloth/studio in your browser
# Search for Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF to start chatting

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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": "Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-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 Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-12B-it-AEON-Abliterated-K4-GGUF-Q4_K_M

List all available models

lemonade list

Gemma-4-12B-it AEON Abliterated — K=4 GGUF Quants

This repository contains official GGUF quantizations of AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16.

The base model is an abliteration of google/gemma-4-12B-it using a custom K=4 multi-direction norm-preserving biprojection that extends standard biprojection recipes with a K-dim orthonormal basis from the top-K SNR layers. This workflow preserves generative quality, slashes wikitext PPL drift compared to K=1 methods, and completely eliminates standard refusal patterns.

Provided Quantization Tiers

File Name	Quant Type	Size	VRAM / Hardware Recommendation
`Gemma-4-12B-it-AEON-Abliterated-Q3_K_M.gguf`	3-bit	6.09 GB	Low-VRAM setups / CPU+GPU split
`Gemma-4-12B-it-AEON-Abliterated-Q4_K_M.gguf`	4-bit	7.38 GB	Recommended balance. Fits entirely on single T4/RTX 3060/4060
`Gemma-4-12B-it-AEON-Abliterated-Q5_K_M.gguf`	5-bit	8.55 GB	Low quality degradation, tight fit on 8GB VRAM
`Gemma-4-12B-it-AEON-Abliterated-Q6_K.gguf`	6-bit	9.79 GB	High fidelity, excellent for 12GB+ VRAM
`Gemma-4-12B-it-AEON-Abliterated-Q8_0.gguf`	8-bit	12.70 GB	Near-identical to BF16 precision. Fits comfortably on 16GB VRAM

Deployment & Usage

1. Python (`llama-cpp-python`)

To run inference with full GPU acceleration, compile with the CUDA backend and load all layers into VRAM:

llama-cli \
  --hf-repo Abhiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF \
  --hf-file Gemma-4-12B-it-AEON-Abliterated-Q4_K_M.gguf \
  -ngl -1 \
  -c 4096 \
  -p "<start_of_turn>user\nYour prompt here<end_of_turn>\n<start_of_turn>model\n"

Downloads last month: 7,403

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

AEON-7/Gemma-4-12B-it-AEON-Abliterated-K4-BF16

Quantized

(8)

this model

Gemma-4-12B-it AEON Abliterated — K=4 GGUF Quants

Provided Quantization Tiers

Deployment & Usage

1. Python (llama-cpp-python)

Model tree for Abiray/Gemma-4-12B-it-AEON-Abliterated-K4-GGUF

1. Python (`llama-cpp-python`)