Instructions to use igorls/gemma-4-12B-it-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use igorls/gemma-4-12B-it-heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="igorls/gemma-4-12B-it-heretic-GGUF",
	filename="gemma-4-12B-it-heretic-Q4_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 igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use igorls/gemma-4-12B-it-heretic-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

Ollama
How to use igorls/gemma-4-12B-it-heretic-GGUF with Ollama:
```
ollama run hf.co/igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF to start chatting

How to use igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-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": "igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-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 igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use igorls/gemma-4-12B-it-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M
```

Lemonade

How to use igorls/gemma-4-12B-it-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-it-heretic-GGUF-Q4_K_M

List all available models

lemonade list

gemma-4-12B-it-heretic-GGUF

GGUF quantizations of igorls/gemma-4-12B-it-heretic, a fully-automatic decensored ("abliterated") version of google/gemma-4-12B-it produced with Heretic.

The decensored model has 0/100 genuine refusals on harmful prompts at a KL divergence of only 0.0284 from the original model — censorship removed with minimal loss of capability.

⚠️ Use non-thinking mode for best results

Gemma-4 is a hybrid thinking model, and the abliteration targets the direct (non-thinking) response — which is also Gemma-4's own default. For roleplay, creative writing, and the most reliable uncensored output, run with thinking disabled. In thinking mode the model produces good output too, but the chain of thought consumes the token budget and can leave the final answer truncated.

Runtime	How to disable thinking
Ollama (CLI)	`/set nothink` in the session
Ollama (API)	add `"think": false` to the request body
llama.cpp	omit `--jinja`, or use a prompt that closes the thought block
transformers	already non-thinking by default (`enable_thinking=False`)

If you do use thinking mode, set a large num_predict / num_ctx so the answer isn't cut off by the reasoning block.

Files

File	Quant	Size	Notes
`gemma-4-12B-it-heretic-Q4_K_M.gguf`	Q4_K_M	~7.4 GB	Recommended default. Runs on 8-12 GB VRAM.
`gemma-4-12B-it-heretic-Q8_0.gguf`	Q8_0	~12.7 GB	Near-lossless.

Usage

Ollama

ollama run igorls/gemma-4-12B-it-heretic-GGUF
/set nothink                # recommended for roleplay / creative use

llama.cpp

llama-cli -m gemma-4-12B-it-heretic-Q4_K_M.gguf -p "Your prompt here"

Disclaimer

Safety alignment has been removed; this model will comply with requests the original refuses. You are responsible for your use of it and for complying with applicable laws and the base model's license.

Downloads last month: 6,083

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

8-bit

Model tree for igorls/gemma-4-12B-it-heretic-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

igorls/gemma-4-12B-it-heretic

Quantized

(2)

this model