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 (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 serve -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 serve -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

OpenClaw new

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

Start the llama.cpp server

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

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

⚠️ Important Notice: UD-Q4_K_XL and Google QAT

UD-Q4_K_XL is the UD team's naming convention for its Gemma QAT Q4_0 GGUF variant. The repository or file name must not be interpreted as proof that the model uses mixed K-quants. In this release, the actual GGUF tensor information shows that the main model weights are stored as Q4_0, while normalization and scaling tensors remain in F32.

This model is derived from Google's QAT weights, which were trained for 4-bit quantization. Claims that this file was converted to UD mixed precision, or that its QAT structure was damaged by mixed K-quant remapping, are therefore not supported by the actual tensor types contained in the GGUF.

Before opening a discussion or reporting a quantization issue, please inspect the GGUF metadata and tensor types first. Do not infer the internal quantization layout from the filename alone, and do not make unsupported technical claims without checking the source information.

✂️ 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