Instructions to use SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF with Ollama:
```
ollama run hf.co/SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF to start chatting

How to use SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL
```

Lemonade

How to use SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/gemma-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-qat-heretic-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SC117/gemma-4-26B-A4B-it-qat-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 SC117/gemma-4-26B-A4B-it-qat-heretic-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-4-26B-A4B-it-qat-heretic-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"

⚡ Gemma 4 26B-A4B Heretic QAT — Q4_0 GGUF

Heretic ARA · QAT-Lossless Q4_0 · 14 GB · MoE 128-Expert (3.8B Active)

📖 中文文档

Q4_0 26B MoE / 3.8B Active Heretic Uncensored 14 GB QAT Weights 128 Experts

Uncensored version of Google Gemma 4 26B-A4B 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	12
end_layer_index	21
preserve_good_behavior_weight	0.3106
steer_bad_behavior_weight	0.0066
overcorrect_relative_weight	0.7982
neighbor_count	14

Metric	Heretic	Original QAT
KL Divergence	0.0660	0 (by definition)
Refusals	13/100	100/100

🏗️ Architecture

Base Model	google/gemma-4-26B-A4B-it
Parameters	25.2B total / 3.8B active (MoE)
Architecture	Mixture-of-Experts: 128 experts, 8 active + 1 shared per token
Layers	30
Hidden Size	2,816
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 (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	14 GB
Effective BPW	4.51 (all weight tensors Q4_0, norms/router 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 85.6% Top-1 vs 70.2% naive Q4_0 (+15.4%)

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. Use --jinja flag and -ngl 99 for GPU offload. Vision support via included mmproj.

llama-server \
  -m gemma-4-26B-A4B-it-qat-heretic-UD-Q4_K_XL.gguf \
  --mmproj mmproj-gemma-4-26B-A4B-it-qat-heretic-BF16.gguf \
  --jinja -ngl 99 -c 8192 \
  --port 8001

🔗 Credits

Heretic Abliteration: coder3101 · Heretic v1.2.0 ARA + Row-Norm
QAT Weights: Google Gemma 4 QAT
Quantization Recipe: Unsloth UD-Q4_K_XL (Q4_0 layout)
Quantization Tool: llama.cpp b9553 · GitHub
Original Model: Google Gemma 4 26B-A4B IT