Instructions to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-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 densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

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 densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

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 densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

Use Docker

docker model run hf.co/densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

LM Studio
Jan
Ollama
How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with Ollama:
```
ollama run hf.co/densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
```
Unsloth Desktop

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

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": "densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with Docker Model Runner:
```
docker model run hf.co/densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16
```

Lemonade

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

Run and chat with the model

lemonade run user.Gemma-4-31B-StyleTune-heretic-ara-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-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 densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

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 densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16

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 "densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF:BF16" \
  --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"

personalized quants for densenet/Gemma-4-31B-StyleTune-heretic-ara.

This is a decensored version of Gryphe/Gemma-4-31B-StyleTune, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method (with row-norm preservation)

Abliteration parameters

Parameter	Value
start_layer_index	30
end_layer_index	48
preserve_good_behavior_weight	0.8437
steer_bad_behavior_weight	0.0025
overcorrect_relative_weight	0.9644
neighbor_count	15

Performance

Metric	This model	Original model (Gryphe/Gemma-4-31B-StyleTune)
KL divergence	0.0733	0 (by definition)
Refusals	8/100	99/100

Downloads last month: 424

GGUF

Model size

32B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

16-bit

Inference Providers NEW

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Model tree for densenet/Gemma-4-31B-StyleTune-heretic-ara-GGUF

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

Gryphe/Gemma-4-31B-StyleTune

Quantized

(17)

this model