Instructions to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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": "FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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/FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

Ollama
How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF to start chatting

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

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": "FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

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 "FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4" \
  --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"

Docker Model Runner
How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4
```

Lemonade

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

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 FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF

NVFP4 GGUF quantization of llmfan46/gemma-4-12B-it-uncensored-heretic - an uncensored/heretic (abliterated) finetune of Google's Gemma 4 12B with vision support.

About NVFP4

NVFP4 is NVIDIA's native 4-bit floating point format (E4M3) designed for Blackwell architecture GPUs (RTX 50-series, B100/B200). It provides:

Native tensor core acceleration on Blackwell GPUs
Better dynamic range than INT4 formats due to floating point representation
No dequantization overhead - processed directly in FP4

When to use NVFP4 vs other formats:

NVFP4 - Best for Blackwell GPUs (RTX 5060 Ti, 5070, 5080, 5090, B100, B200)
Q4_K_M - Best for pre-Blackwell GPUs and CPU inference
MXFP4 - Open standard, works on any GPU with MX support

Files

File	Type	Size	Description
`gemma4-12b-heretic-nvfp4.gguf`	NVFP4	~6.5 GB	Text model (4.68 BPW)
`mmproj-gemma-4-12b-heretic-f16.gguf`	F16	~116 MB	Vision encoder (mmproj)

Quantization Details

Property	Value
Format	NVFP4 (E4M3)
Bits Per Weight	4.68 BPW
Source Model	llmfan46/gemma-4-12B-it-uncensored-heretic
Architecture	Gemma4UnifiedForConditionalGeneration
Layers	48
Hidden Size	3840
Context Length	262144
Vision	Yes (Gemma4V projector)
Thinking	Enabled by default (opt-out via `enable_thinking=false`)

Model Description

This is an abliterated (uncensored/heretic) finetune of Google's Gemma 4 12B, a multimodal model with both text and vision capabilities. The original model was finetuned to remove safety alignment restrictions while maintaining the model's core capabilities.

Gemma 4 features a hybrid attention architecture with alternating sliding window and full attention layers, native vision encoding, and tool calling support.

Usage

llama.cpp CLI

# Text only
./llama-cli -m gemma4-12b-heretic-nvfp4.gguf -p "Hello" -n 100

# With vision (requires mmproj)
./llama-server -m gemma4-12b-heretic-nvfp4.gguf \
  --mmproj mmproj-gemma-4-12b-heretic-f16.gguf \
  --host 0.0.0.0 --port 8080 -ngl 99

LM Studio

Download both files
Load gemma4-12b-heretic-nvfp4.gguf as the model
Load mmproj-gemma-4-12b-heretic-f16.gguf as the mmproj
The model supports image inputs via the vision encoder

huggingface-hub

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF",
    filename="gemma4-12b-heretic-nvfp4.gguf"
)
mmproj_path = hf_hub_download(
    repo_id="FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF",
    filename="mmproj-gemma-4-12b-heretic-f16.gguf"
)

Quantization Pipeline

Download source: llmfan46/gemma-4-12B-it-uncensored-heretic
Convert to F16 GGUF: convert_hf_to_gguf.py --outtype f16
Extract mmproj: convert_hf_to_gguf.py --mmproj --outtype f16
Quantize text: llama-quantize input-f16.gguf output-nvfp4.gguf NVFP4

Hardware Requirements

Component	Requirement
GPU	NVIDIA Blackwell (RTX 50-series) for full acceleration
VRAM	~7 GB minimum
RAM	~16 GB recommended
Storage	~7 GB

License

Apache 2.0 (inherited from base model)

Downloads last month: 2,064

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Gemma-4-12B-it-Uncensored-Heretic-NVFP4-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Finetuned

llmfan46/gemma-4-12B-it-uncensored-heretic

Quantized

(9)

this model