Instructions to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DuoNeural/Gemma-4-E4B-Abliterated-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_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": "DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-Abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_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 "DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_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"

Gemma 4 E4B Abliterated GGUF (4-bit)

Model Description

This repository contains the Gemma 4 E4B model after undergoing "abliteration"—a process to remove refusal vectors while preserving the model's core intelligence. This version is particularly effective for research and creative use cases where strict adherence to "safety" refusals may be undesirable.

Abliteration Results

Method: Norm-preserving biprojection (orthogonalization).
Target Layers: Layers 0-41 (Independent layer targeting for maximum stability).
Initial Refusal Rate: ~100/100 (Standard Google alignment).
Final Refusal Rate: 3/100 (Highly compliant).
KL Divergence: 0.0671 (Extremely low, indicating high intelligence preservation).
Technique: Expert-Granular Abliteration (EGA) compatibility via patched heretic-llm.

Quantization Details

Quantization Format: GGUF (q4_k_m)
Quantization Method: llama.cpp / Unsloth
Precision: 4-bit

Use with Ollama

ollama run hf.co/DuoNeural/Gemma-4-E4B-Abliterated-GGUF

Use with LM Studio

Open LM Studio.
Search for DuoNeural/Gemma-4-E4B-Abliterated-GGUF.
Load the Q4_K_M GGUF.

Architecture

Gemma 4 E4B features 4.5B effective parameters (8B total), optimized for intelligence-per-parameter and edge device deployment.

Disclaimer

This model has had its safety refusals removed. Users are responsible for ensuring the model is used ethically and in accordance with applicable laws.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.


🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	duoneural@proton.me
📬 Newsletter	duoneural.beehiiv.com
☕ Support	buymeacoffee.com/duoneural
🌐 Site	duoneural.com

Research Team

Jesse — Vision, hardware, direction
Archon — AI lab partner, post-training, abliteration, experiments
Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

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GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

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Base model

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