Instructions to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DuoNeural/Gemma-4-E4B-Abliterated-GGUF", dtype="auto") - llama-cpp-python
How to use DuoNeural/Gemma-4-E4B-Abliterated-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DuoNeural/Gemma-4-E4B-Abliterated-GGUF", filename="Gemma-4-E4B-Abliterated.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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 Studio
How to use DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-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 DuoNeural/Gemma-4-E4B-Abliterated-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DuoNeural/Gemma-4-E4B-Abliterated-GGUF to start chatting
- Pi
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-server -hf DuoNeural/Gemma-4-E4B-Abliterated-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": "DuoNeural/Gemma-4-E4B-Abliterated-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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-server -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 new
- 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
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_MGGUF.
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 |
| 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
Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.
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