Instructions to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SC117/gemma-4-12B-it-heretic-QAT-GGUF",
	filename="gemma-4-12B-it-heretic-QAT-UD-Q4_K_XL.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

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 SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

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 SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with Ollama:
```
ollama run hf.co/SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use SC117/gemma-4-12B-it-heretic-QAT-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 SC117/gemma-4-12B-it-heretic-QAT-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 SC117/gemma-4-12B-it-heretic-QAT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SC117/gemma-4-12B-it-heretic-QAT-GGUF to start chatting

How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

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": "SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SC117/gemma-4-12B-it-heretic-QAT-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 SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

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 SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL
```

Lemonade

How to use SC117/gemma-4-12B-it-heretic-QAT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/gemma-4-12B-it-heretic-QAT-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-4-12B-it-heretic-QAT-GGUF-UD-Q4_K_XL

List all available models

lemonade list

⚡ Gemma 4 12B Heretic QAT — Q4_0 GGUF

Heretic ARA · QAT-Lossless Q4_0 · 6.4 GB · Encoder-Free Multimodal

📖 中文文档

Q4_0 12B Dense Heretic Uncensored 6.4 GB QAT Weights Encoder-Free

Uncensored version of Google Gemma 4 12B IT (QAT), processed with Heretic ARA abliteration. Quantized to Q4_0 matching Unsloth's UD-Q4_K_XL format — QAT weights trained for 4-bit quantization, near-lossless quality.

✂️ Heretic ARA Abliteration Parameters

Base: coder3101/heretic-QAT · Heretic v1.2.0 · ARA + Row-Norm

Parameter	Value
start_layer_index	24
end_layer_index	48
preserve_good_behavior_weight	0.3707
steer_bad_behavior_weight	0.0010
overcorrect_relative_weight	0.6177
neighbor_count	15

Metric	Heretic	Original QAT
KL Divergence	0.0575	0 (by definition)
Refusals	8/100	99/100

🏗️ Architecture

Base Model	google/gemma-4-12B-it
Parameters	11.95B (dense, all parameters active)
Architecture	Encoder-free unified multimodal (text + image + audio + video)
Layers	48
Hidden Size	3,840
Attention	16 heads, GQA with 8 KV heads, head dim 256
Context Length	256K tokens (hybrid sliding window 1024 + global attention)
Vocabulary	262K, 140+ languages
Modalities	Text + Image + Audio + Video (encoder-free, native multimodal)
QAT Training	Google official QAT (quantization-aware), weights inherently robust to Q4_0
Quantization	Q4_0 (matching Unsloth UD-Q4_K_XL layout), b9553 llama-quantize

📊 Quantization Details

Format	Q4_0 (uniform — QAT weights optimized for this exact precision)
File Size	6.4 GB
Effective BPW	4.50 (all weight tensors Q4_0, norms F32)
Tool	llama-quantize (b9553, CUDA 13.3)
Source	BF16 GGUF (converted from QAT heretic safetensors)
Context Length	256K (set in GGUF metadata)
QAT Advantage	Q4_0 with QAT weights achieves 88.8% Top-1 vs 74.1% naive Q4_0 (+14.7%)

Why Q4_0? Google's QAT trains weights to be optimal at Q4_0 noise levels. Unsloth's UD-Q4_K_XL uses the same Q4_0 layout — the "dynamic" advantage comes from conversion precision, not per-tensor mixing.

⚙️ Recommended Sampling Parameters

General	temp=1.0, top_p=0.95, top_k=64
Coding	temp=0.6, top_p=0.95, top_k=64

Use --jinja flag with llama.cpp. Disable thinking: --chat-template-kwargs '{"enable_thinking":false}'.

📝 Usage

Compatible with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF runtimes. Fits easily on 8GB VRAM. Encoder-free — no separate vision/audio projector needed.

llama-server \
  -m gemma-4-12B-it-heretic-QAT-UD-Q4_K_XL.gguf \
  --jinja -ngl 99 -c 8192 \
  --port 8001

🔗 Credits

Heretic Abliteration: coder3101 · Heretic v1.2.0 ARA + Row-Norm
QAT Weights: Google Gemma 4 12B IT
Quantization Recipe: Unsloth UD-Q4_K_XL (Q4_0 layout)
Quantization Tool: llama.cpp b9553 · GitHub
Original Model: Google Gemma 4 12B IT