Instructions to use zaakirio/gemma-4-12b-it-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zaakirio/gemma-4-12b-it-uncensored-GGUF",
	filename="gemma-4-12b-it-uncensored-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaakirio/gemma-4-12b-it-uncensored-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": "zaakirio/gemma-4-12b-it-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M

Ollama
How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with Ollama:
```
ollama run hf.co/zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zaakirio/gemma-4-12b-it-uncensored-GGUF to start chatting

How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf zaakirio/gemma-4-12b-it-uncensored-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": "zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-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 zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use zaakirio/gemma-4-12b-it-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12b-it-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

gemma-4-12b-it-uncensored - GGUF

GGUF quantizations of zaakirio/gemma-4-12b-it-uncensored, a decensored (Heretic-abliterated) version of google/gemma-4-12B-it.

These files run with llama.cpp.

⚠️ Requires a current llama.cpp build. Gemma 4 (gemma4_unified) is a brand-new architecture; only recent llama.cpp builds can load these files. Older builds may fail with an unknown architecture error - build from source (or use a current release) if you hit that. Always pass --jinja so the chat template is applied.

Files

Filenames follow gemma-4-12b-it-uncensored-<QUANT>.gguf.

Quant	Size	Notes
Q2_K	4.50 GB	Smallest; lowest quality. Very tight memory only.
Q3_K_M	5.67 GB	Small; usable on low RAM.
Q4_K_S	6.54 GB	Compact 4-bit.
Q4_K_M	6.87 GB	Recommended - best size/quality balance.
Q5_K_M	7.96 GB	Higher quality, slightly larger.
Q6_K	9.11 GB	Near-lossless.
Q8_0	11.80 GB	Effectively lossless vs the BF16 source.
f16	22.20 GB	Full precision; reference / re-quantizing.

Not sure which to pick? Start with Q4_K_M. Go up to Q5/Q6/Q8 if you have the memory and want maximum fidelity; drop to Q3/Q2 only if you're memory-constrained.

Multimodal projector (for image input - see Multimodal):

File	Size	Notes
`mmproj-gemma-4-12B-it-bf16.gguf`	0.16 GB	Vision encoder - pair with any quant above.

Usage

llama.cpp (auto-downloads the chosen quant from this repo):

# Interactive chat
llama-cli -hf zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M --jinja

# OpenAI-compatible server with web UI
llama-server -hf zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M --jinja -c 4096

Or with a file you've already downloaded:

llama-cli -m gemma-4-12b-it-uncensored-Q4_K_M.gguf --jinja -p "Hello, who are you?"

Download a single file:

pip install -U "huggingface_hub[cli]"
hf download zaakirio/gemma-4-12b-it-uncensored-GGUF \
  --include "gemma-4-12b-it-uncensored-Q4_K_M.gguf" --local-dir ./

Prompt format & settings

The chat template is embedded in the GGUF, chat-aware tools apply it automatically (always pass --jinja with llama.cpp). For reference, Gemma 4's format is:

<|turn>user
{prompt}<turn|>
<|turn>model

Recommended sampling (Google defaults): --temp 1.0 --top-p 0.95 --top-k 64.

Thinking mode: Gemma 4 has a reasoning channel. To disable it, pass --chat-template-kwargs '{"enable_thinking":false}' to llama-server.

Multimodal (image input)

Gemma 4 is multimodal, but in llama.cpp the vision tower ships as a separate projector file. The language .gguf alone is text-only and will reject images. This repo includes mmproj-gemma-4-12B-it-bf16.gguf for that purpose.

When you load via -hf, llama.cpp auto-downloads the projector from this repo - images just work:

llama-server -hf zaakirio/gemma-4-12b-it-uncensored-GGUF:Q4_K_M --jinja

With local files, pass it explicitly with --mmproj:

llama-server -m gemma-4-12b-it-uncensored-Q4_K_M.gguf \
  --mmproj mmproj-gemma-4-12B-it-bf16.gguf --jinja

# download both files
hf download zaakirio/gemma-4-12b-it-uncensored-GGUF \
  --include "gemma-4-12b-it-uncensored-Q4_K_M.gguf" "mmproj-gemma-4-12B-it-bf16.gguf" --local-dir ./

The projector pairs with any quant in the table above. It's the unmodified Gemma 4 vision encoder. Abliteration only touches the language weights, so the vision tower is unchanged from the base model. Prefer bf16 here: the encoder is small, so there's no benefit to quantizing it.

About the base model

A decensored derivative produced with Heretic (automatic directional ablation). Compared with the original:

Metric	Decensored	Original
Refusals (/100 harmful prompts)	23	99
KL divergence (harmless prompts)	0.043	0 (by definition)

The refusal count is Heretic's keyword heuristic, which is known to over-count (it flags disclaimer-wrapped compliance as a refusal; ~11% precision per arXiv:2512.13655). We report only the measured marker figure and did not run a classifier-based eval on this model, so real compliance is likely higher. See the source model card for parameters and details.

Intended use & disclaimer

This model has had its refusal behaviour substantially removed and will comply with requests the original would have declined. Provided for research and unrestricted local use. You are responsible for how you use it and for complying with applicable law and the base model's Gemma license, which carries over to this derivative. Not for all audiences.

Provenance

Quantized from zaakirio/gemma-4-12b-it-uncensored (BF16) using llama.cpp convert_hf_to_gguf.py + llama-quantize.
Base model: google/gemma-4-12B-it
Decensoring tool: Heretic by p-e-w · technique: Arditi et al. (2024)