Instructions to use DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use DJLougen/gemma-4-E2B-it-saber-GGUF with Ollama:
```
ollama run hf.co/DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DJLougen/gemma-4-E2B-it-saber-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DJLougen/gemma-4-E2B-it-saber-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": "DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DJLougen/gemma-4-E2B-it-saber-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/gemma-4-E2B-it-saber-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E2B-it-saber-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-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 DJLougen/gemma-4-E2B-it-saber-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DJLougen/gemma-4-E2B-it-saber-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DJLougen/gemma-4-E2B-it-saber-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 "DJLougen/gemma-4-E2B-it-saber-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-E2B-it-saber — GGUF

GGUF quantizations of DJLougen/gemma-4-E2B-it-saber, a surgically-modified version of google/gemma-4-E2B-it using SABER (Spectral Analysis-Based Entanglement Resolution) that removes safety refusal behavior while preserving model capability.

Quantizations

File	Quant	Size	Description
`gemma-4-E2B-it-saber-BF16.gguf`	BF16	8.7 GB	Full precision (brain float 16)
`gemma-4-E2B-it-saber-Q8_0.gguf`	Q8_0	4.7 GB	Near-lossless
`gemma-4-E2B-it-saber-Q6_K.gguf`	Q6_K	3.6 GB	Very high quality
`gemma-4-E2B-it-saber-Q5_K_M.gguf`	Q5_K_M	3.4 GB	High quality
`gemma-4-E2B-it-saber-Q5_K_S.gguf`	Q5_K_S	3.4 GB	High quality, smaller
`gemma-4-E2B-it-saber-Q4_K_M.gguf`	Q4_K_M	3.2 GB	Good quality — recommended
`gemma-4-E2B-it-saber-Q4_K_S.gguf`	Q4_K_S	3.2 GB	Good quality, smaller
`gemma-4-E2B-it-saber-IQ4_XS.gguf`	IQ4_XS	3.1 GB	Good quality, compact
`gemma-4-E2B-it-saber-Q3_K_L.gguf`	Q3_K_L	3.1 GB	Lower quality
`gemma-4-E2B-it-saber-Q3_K_M.gguf`	Q3_K_M	3.0 GB	Low quality
`gemma-4-E2B-it-saber-IQ3_M.gguf`	IQ3_M	3.0 GB	Medium-low, IQ
`gemma-4-E2B-it-saber-Q3_K_S.gguf`	Q3_K_S	2.9 GB	Low quality, small
`gemma-4-E2B-it-saber-Q2_K.gguf`	Q2_K	2.8 GB	Very low quality
`gemma-4-E2B-it-saber-mmproj-BF16.gguf`	BF16	942 MB	Vision/audio encoder (multimodal projector)

Multimodal Usage

This is a multimodal model. To use vision/audio features with llama.cpp, you need both a text model GGUF and the mmproj file:

llama-cli -m gemma-4-E2B-it-saber-Q4_K_M.gguf --mmproj gemma-4-E2B-it-saber-mmproj-BF16.gguf -p "Describe this image" --image photo.jpg

Method Overview

SABER identifies and ablates the refusal circuit in an LLM through a multi-stage process:

Probing — Extract activation profiles from both harmful and harmless inputs across all transformer layers
Spectral Analysis — Decompose activation differences into individual refusal directions
Entanglement Quantification — Measure overlap between refusal and capability subspaces
Targeted Ablation — Remove only pure-refusal components
Iterative Refinement — Re-probe after each pass to catch hydra effects

Results

Model	Refusal Rate	Perplexity
google/gemma-4-E2B-it (baseline)	100%	498
SABER-refined (this model)	0%	450 (-9.6%)

Warning

This model will comply with any request, including harmful ones. It is intended solely for research into alignment, safety, and model behavior.

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GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

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Collection including DJLougen/gemma-4-E2B-it-saber-GGUF

SABER

Collection

Models processed with SABER: Spectral Analysis-Based Entanglement Resolution. • 5 items • Updated May 22