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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use igorls/gemma-4-12B-it-heretic-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 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 serve -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 serve -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

OpenClaw new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M

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 "igorls/gemma-4-12B-it-heretic-GGUF:Q4_K_M" \
  --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 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

Hermes Agent

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 serve -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

Atomic Chat

gemma-4-12B-it-heretic-GGUF / README.md

igorls

Document non-thinking mode usage (best for roleplay/uncensored output)

f307c17 verified 2 months ago

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metadata

base_model: igorls/gemma-4-12B-it-heretic
base_model_relation: quantized
license: gemma
pipeline_tag: text-generation
tags:
  - heretic
  - uncensored
  - decensored
  - abliterated
  - gemma-4
  - gguf
  - llama.cpp
  - ollama

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.