Instructions to use SC117/Gemma-4-12B-it-heretic-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-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-GGUF",
	filename="Gemma-4-12B-it-heretic-Q4_K_M.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-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SC117/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-server -hf SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SC117/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 SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SC117/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 SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SC117/Gemma-4-12B-it-heretic-GGUF with Ollama:
```
ollama run hf.co/SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M
```

Unsloth Studio

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

How to use SC117/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-server -hf SC117/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": "SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SC117/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-server -hf SC117/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 SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SC117/Gemma-4-12B-it-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/Gemma-4-12B-it-heretic-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/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

GGUF Heretic Multimodal

Gemma-4-12B-it-heretic-GGUF

English | 📖 中文文档

🧠 Uncensored · Encoder-Free Multimodal · Vision + Audio

Uncensored version of Google Gemma 4 12B IT, processed with Heretic ARA+LoRA abliteration. Encoder-free multimodal architecture supporting text, image, audio, and video inputs natively.

Base model: google/gemma-4-12B-it · Parameters: 11.95B · Layers: 48 · Context: 256K · Vocab: 262K

📢 Update (June 7, 2026)

Context length fix: all quantized files have been re-generated with correct 256K context metadata. Previously downloaded models were capped at 128K context. If you need 256K long context, please re-download.

⚙️ Heretic ARA+LoRA Abliteration

Method: Arbitrary-Rank Ablation with LoRA (ARA+LoRA) — Heretic v2

Trial: 177 / 200 (optimized)

Parameter	Value
Layer Range	8 – 47 (40 of 48 layers)
preserve_good_behavior_weight	0.3894
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	0.6769
neighbor_count	12
KL Divergence	0.0550 (low = minimal behavior drift)
Refusals	15 / 100 (85% reduction from baseline)

📦 GGUF Quantization Files

File	Type	BPW	Size
Gemma-4-12B-it-heretic-Q4_K_M.gguf	Q4_K_M	4.95	6.9 GB
Gemma-4-12B-it-heretic-Q6_K.gguf	Q6_K	6.50	9.2 GB
Gemma-4-12B-it-heretic-Q8_0.gguf	Q8_0	8.50	12 GB
Gemma-4-12B-it-heretic-f16.gguf	F16	16.00	23 GB

Quantized with llama.cpp b9535 (CUDA 13.3). Recommended: Q4_K_M for daily use, Q8_0 for quality-critical tasks.

🧠 Architecture

Gemma 4 12B uses a unified, encoder-free architecture — no separate vision or audio encoders. All modalities (text, image, audio, video) are processed directly by the decoder-only transformer via linear projection layers.

Feature	Value
Architecture	gemma4 (Dense, decoder-only)
Parameters	11.95B
Layers	48
Hidden Size	3840
Attention Heads	16 (GQA with 8 KV heads)
Context Length	256K tokens
Sliding Window	1024 (hybrid local + global attention)
Vocabulary	262,144 tokens
Modalities	Text, Image, Audio, Video
License	Apache 2.0

📊 Original Model Benchmarks (google/gemma-4-12B-it)

Reference scores from Google DeepMind. Heretic abliteration may slightly affect benchmark performance.

Benchmark	Gemma 4 12B	Gemma 3 27B
MMLU Pro	77.2%	67.6%
AIME 2026 (no tools)	77.5%	20.8%
LiveCodeBench v6	72.0%	29.1%
Codeforces ELO	1659	110
GPQA Diamond	78.8%	42.4%
MMMU Pro (Vision)	69.1%	49.7%
MATH-Vision	79.7%	46.0%

🚀 Recommended Sampling

Parameter	Value
temperature	1.0
top_p	0.95
top_k	64

📝 Notes

• This is an abliterated (uncensored) model. It may produce content that the original model would refuse. Use responsibly.

• Original model: google/gemma-4-12B-it (Apache 2.0)

Downloads last month: 2,960

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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Model tree for SC117/Gemma-4-12B-it-heretic-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(153)

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