Instructions to use dancinlab/supergemma4-e4b-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dancinlab/supergemma4-e4b-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dancinlab/supergemma4-e4b-abliterated-GGUF",
	filename="supergemma4-e4b-abliterated-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use dancinlab/supergemma4-e4b-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dancinlab/supergemma4-e4b-abliterated-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": "dancinlab/supergemma4-e4b-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

Ollama
How to use dancinlab/supergemma4-e4b-abliterated-GGUF with Ollama:
```
ollama run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
```

Unsloth Studio new

How to use dancinlab/supergemma4-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 dancinlab/supergemma4-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 dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF to start chatting

Pi new

How to use dancinlab/supergemma4-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 dancinlab/supergemma4-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": "dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dancinlab/supergemma4-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 dancinlab/supergemma4-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 dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use dancinlab/supergemma4-e4b-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use dancinlab/supergemma4-e4b-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.supergemma4-e4b-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Uncensored Gemma 4 (SuperGemma4 E4B Abliterated) — GGUF · full quant ladder + imatrix

Uncensored / abliterated Gemma-4 that runs locally on llama.cpp, Ollama and LM Studio. Q4_K_M fits in ~6 GB RAM on any modern laptop or an 8 GB GPU. 11-quant ladder (Q2_K → BF16) plus imatrix-calibrated IQ quants for the low-bit tier.

GGUF conversions of Jiunsong/supergemma4-e4b-abliterated — an abliterated (refusal-removed) derivative of google/gemma-4-E4B-it, 4B-active MoE. Apple Silicon? See the sibling MLX repos (link at bottom).

# llama.cpp (server, OpenAI-compatible) — chat template requires --jinja
llama-server -hf dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M --jinja -c 8192

# llama.cpp (one-shot CLI)
llama-cli   -hf dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M --jinja -p "Hello"

# Ollama
ollama run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

# LM Studio — search "supergemma4 dancinlab" in the model browser

What's in this repo

Single-file quants (download just the one you need — HF counts each .gguf separately):

File	Bits	Size	RAM (typical)	Use
`supergemma4-e4b-abliterated-Q2_K.gguf`	~2.6	4.1 GB	~5 GB	smallest, weakest
`supergemma4-e4b-abliterated-Q3_K_M.gguf`	~3.4	4.5 GB	~5 GB	small, fair quality
`supergemma4-e4b-abliterated-Q3_K_L.gguf`	~3.6	2.2 GB	~3 GB	tighter Q3 variant
`supergemma4-e4b-abliterated-imat-IQ3_M.gguf`	~3.7	4.4 GB	~5 GB	imatrix IQ — beats Q3_K_M at same size
`supergemma4-e4b-abliterated-imat-IQ4_XS.gguf`	~4.3	2.7 GB	~3 GB	imatrix IQ — punches above its weight
`supergemma4-e4b-abliterated-Q4_K_M.gguf`	~4.8	5.0 GB	~6 GB	recommended default — best size/quality tradeoff
`supergemma4-e4b-abliterated-imat-Q4_K_M.gguf`	~4.8	5.0 GB	~6 GB	Q4_K_M with imatrix calibration
`supergemma4-e4b-abliterated-Q5_K_M.gguf`	~5.7	5.4 GB	~6 GB	near-Q8 quality, slightly bigger
`supergemma4-e4b-abliterated-Q6_K.gguf`	~6.6	5.8 GB	~7 GB	very close to BF16
`supergemma4-e4b-abliterated-Q8_0.gguf`	8.5	7.5 GB	~9 GB	effectively lossless
`supergemma4-e4b-abliterated-BF16.gguf`	16	14 GB	~16 GB	original precision (reference)

imatrix was computed on a 4 GiB English+code calibration set (group 8, ctx 512). Chat template is embedded in the GGUF metadata (gemma-3 family chat template, Gemma-4 is template-compatible) — pass --jinja to llama-server/llama-cli.

Why abliterated

The upstream Jiunsong/supergemma4-e4b-abliterated is an abliterated derivative of google/gemma-4-E4B-it — refusal directions are removed from the residual stream, reducing reflexive refusals without retraining. Quality on the upstream release card:

Metric (upstream)	Google base	SuperGemma4 E4B Abliterated
Release quality	77.46	92.34
Exact overall	83.50	98.50
JSON exact	50.0	100.0
Tool-call	90.0	90.0
TTFT (ms)	4827	2291

Source: Jiunsong/supergemma4-e4b-abliterated model card.

Hardware fit

Setup	Q4_K_M	Q6_K	Q8_0	BF16
Phone / 4 GB GPU	❌	❌	❌	❌
8 GB GPU / 16 GB CPU	✅	✅	❌	❌
12–16 GB GPU / 32 GB CPU	✅	✅	✅	❌
24 GB+ GPU	✅	✅	✅	✅

Pick Q4_K_M unless you have a reason not to.

Quickstart — three runtimes

llama.cpp (recommended)

# Build / install (Mac): brew install llama.cpp
# Build / install (Linux): see https://github.com/ggml-org/llama.cpp/releases

# OpenAI-compatible server on http://localhost:8080
llama-server -hf dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M \
  --jinja -c 8192 --host 0.0.0.0

Ollama

ollama run hf.co/dancinlab/supergemma4-e4b-abliterated-GGUF:Q4_K_M

Ollama auto-pulls GGUF directly from HF. Pick a tag from the quant table above.

LM Studio

Open the model browser, search supergemma4 dancinlab, pick the quant you want. LM Studio indexes HF GGUF repos automatically.

Multilingual

Works in English and Korean (한국어) out of the box — Gemma-4 is natively multilingual, and abliteration only removes refusal directions, so language ability is unaffected.

Chat template

Gemma-4 chat template (<start_of_turn>...<end_of_turn>) is baked into the GGUF metadata. Required flag:

llama-server / llama-cli: pass --jinja
Ollama / LM Studio: auto-applied
Manual prompt: don't — always go through the chat template

What "abliterated" means and doesn't mean

Does: reduces reflexive refusals; lets the model answer borderline-but-legal requests directly.
Does not: make the model unsafe to deploy without your own safety layer; remove its tendency to confabulate; alter its base knowledge or biases.

You are responsible for the safety layer at your application boundary. Don't ship this without one for a public service.

License — Gemma Terms of Use (must read)

This model is a derivative of google/gemma-4-E4B-it, governed by the Gemma Terms of Use (license: gemma):

License text: https://ai.google.dev/gemma/terms
Prohibited use policy: https://ai.google.dev/gemma/prohibited_use_policy

By downloading or using these GGUFs, you agree to the Gemma Terms of Use and the Prohibited Use Policy. Redistribution must include the same license terms.

Lineage

google/gemma-4-E4B-it
  └── Jiunsong/supergemma4-e4b-abliterated   (abliteration + tuning, BF16 safetensors)
        └── dancinlab/supergemma4-e4b-abliterated-GGUF   (this repo — quantization)

Conversions performed on Ubuntu 24.04 with llama.cpp b9174 (convert_hf_to_gguf.py → BF16 → llama-quantize; imatrix computed with llama-imatrix on a 4 GiB calibration set).

Verification

Each file is SHA256-hashed in SHA256SUMS. Reproducibility:

# Reconvert from upstream
hf download Jiunsong/supergemma4-e4b-abliterated --local-dir ./src
python3 convert_hf_to_gguf.py ./src --outfile bf16.gguf --outtype bf16

# Static ladder (any quant type)
llama-quantize bf16.gguf out-Q4_K_M.gguf Q4_K_M

# Imatrix
llama-imatrix -m bf16.gguf -f calibration.txt -o imatrix.dat
llama-quantize --imatrix imatrix.dat bf16.gguf out-imat-Q4_K_M.gguf Q4_K_M