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

File size: 2,396 Bytes

---
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](https://huggingface.co/igorls/gemma-4-12B-it-heretic),
a fully-automatic decensored ("abliterated") version of
[google/gemma-4-12B-it](https://huggingface.co/google/gemma-4-12B-it) produced
with [Heretic](https://github.com/p-e-w/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

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

### llama.cpp

```bash
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](https://ai.google.dev/gemma/terms).