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

supergemma4-e4b-abliterated-GGUF / README.md

dancinlife

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	---
	license: gemma
	license_link: https://ai.google.dev/gemma/terms
	library_name: gguf
	pipeline_tag: text-generation
	base_model: Jiunsong/supergemma4-e4b-abliterated
	base_model_relation: quantized
	language:
	- en
	- ko
	tags:
	- gemma
	- gemma-4
	- gemma4
	- abliterated
	- uncensored
	- uncensored-llm
	- no-refusal
	- gguf
	- llama.cpp
	- ollama
	- lm-studio
	- quantized
	- imatrix
	- conversational
	- roleplay
	- text-generation
	- mac
	- cpu
	- local
	quantized_by: dancinlab
	inference: false
	---

	# 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`](https://huggingface.co/Jiunsong/supergemma4-e4b-abliterated)
	— an abliterated (refusal-removed) derivative of
	[`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it),
	4B-active MoE. Apple Silicon? See the sibling MLX repos (link at bottom).

	```bash
	# 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`](https://huggingface.co/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)

	```bash
	# 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

	```bash
	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:

	```bash
	# 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
	```

	## Credits

	- Upstream model: [`Jiunsong`](https://huggingface.co/Jiunsong)
	- Original base: [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it)
	- Quantization, imatrix, and packaging: [`dancinlab`](https://huggingface.co/dancinlab)

	Sibling repo (Apple Silicon): [`dancinlab/supergemma4-e4b-abliterated-MLX`](https://huggingface.co/dancinlab/supergemma4-e4b-abliterated-MLX) — bf16 / 4bit / 8bit.

	Collection: [`dancinlab/uncensored`](https://huggingface.co/collections/dancinlab/uncensored-6a080743e6774450ba77a427).