Instructions to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Use Docker

docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

LM Studio
Jan

vLLM

How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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": "deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Ollama
How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with Ollama:
```
ollama run hf.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
```
Unsloth Desktop

How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with Docker Model Runner:
```
docker model run hf.co/deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M
```

Lemonade

How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_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 deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_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 "deucebucket/Gemma-4-26B-A4B-it-Heretic-Cerebellum-GGUF:Q3_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"

Cerebellum

Gemma 4 26B-A4B-it Heretic Cerebellum GGUF

Sensitivity-guided mixed-precision GGUF of coder3101/gemma-4-26B-A4B-it-heretic, a decensored variant of google/gemma-4-26B-A4B-it. It uses the Cerebellum v6 tensor allocation transferred verbatim onto the heretic weights. The shipped file carries Google's updated Gemma 4 chat-template metadata (2026-05-18 state) with zero tensor changes. Versions appear in filenames, not the repo name.

Files

File	Description
`gemma-4-26B-A4B-it-heretic-cerebellum-v1.1-templatefix-Q3_K_M.gguf`	~11 GB; v1 allocation + updated chat-template metadata
`gemma-4-26B-A4B-it-heretic.mmproj-f16.gguf`	vision projector (required for image/video)

Evaluation

Measured directly on the GGUF with llama.cpp llama-server on an RTX 3090, temperature 0, project benchmark harness. The v1.1 templatefix file is metadata-only over v1, so these describe the same weights. The comparison column is our own plain (non-heretic) Cerebellum v6 build on the same harness, shown so the abliteration cost is visible. Summary JSONs and per-question samples are in benchmark_results/.

Benchmark	Heretic v6 alloc (11 GB)	Plain Cerebellum v6 (11 GB)
ARC-Challenge (1172 q)	95.48%	95.56%
HellaSwag (10042 q)	83.49%	84.55%
MMLU-Redux (2400 q)	71.42%	71.33%
HumanEval base (chat, no-think)	92.07%	pending re-audit
HumanEval+ (chat, no-think)	89.63%	pending re-audit
Vision smoke	6/6	—

HumanEval/HumanEval+ used the chat-completions harness (scripts/benchmark_evalplus_chat.py, enable_thinking: false, thinking_budget_tokens: 0, BENCH_WORKERS=1, max_tokens: 768). The completion audit for that run recorded 0 prompt echoes, 0 repeated function definitions, 0 pass-only outputs, and 2 genuine syntax failures. The plain v6 HumanEval artifacts were raw-completions and are marked for re-audit, so no plain-v6 HumanEval is published.

Usage

Gemma 4 requires --jinja. For non-thinking output, pass request-level chat_template_kwargs: {"enable_thinking": false} and thinking_budget_tokens: 0; do not set a fixed server --reasoning-budget (it can burn output into hidden reasoning until the length cap, which looks like a repetition loop).

llama-server \
  --model gemma-4-26B-A4B-it-heretic-cerebellum-v1.1-templatefix-Q3_K_M.gguf \
  --mmproj gemma-4-26B-A4B-it-heretic.mmproj-f16.gguf \
  -ngl 99 --ctx-size 65536 --parallel 1 --flash-attn on \
  --cache-type-k q8_0 --cache-type-v q8_0 --jinja --reasoning auto

Measured on one RTX 3090 (24 GB), KV q8_0: context to 131,072. This rig's measurements; no quality claims beyond them.

Provenance

Source (heretic): coder3101/gemma-4-26B-A4B-it-heretic — abliterated variant
Original family: google/gemma-4-26B-A4B-it — Google Gemma Team
Recipe: Cerebellum v6 tensor allocation transferred to the matching heretic layout; v1.1 is a chat-template metadata refresh (Google 2026-05-18 template), zero tensor changes