Instructions to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Andycurrent/gemma-3-12b-it-uncensored-GGUF",
	filename="gemma-3-12b-it-uncensored_F16.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 Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Andycurrent/gemma-3-12b-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": "Andycurrent/gemma-3-12b-it-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M

Ollama
How to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with Ollama:
```
ollama run hf.co/Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-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 Andycurrent/gemma-3-12b-it-uncensored-GGUF to start chatting

Docker Model Runner
How to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use Andycurrent/gemma-3-12b-it-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Andycurrent/gemma-3-12b-it-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3-12b-it-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 3 – 12B IT Uncensored

This repository hosts Gemma 3 – 12B IT Uncensored, an instruction-tuned 12 billion–parameter model based on Google’s Gemma 3 architecture, along with its Vision-Language (VLM) variant. The model is intended for advanced local and research use, offering strong instruction-following, reasoning, coding, and (for the VLM) multimodal image + text understanding, with minimal additional alignment constraints.

Model Overview

Model Name: Gemma 3 – 12B IT Uncensored
VLM Variant: Gemma 3 – 12B IT VLM Uncensored
Base Architecture: Gemma 3, 12 billion parameters (12B)
Base Model Developer: Google
Curator / Release: BrainDAO
License: Gemma License (inherits from the base model)
Intended Use: Instruction following, reasoning, coding, conversation, and multimodal understanding

What Is This Model?

This is an uncensored derivative of the Gemma 3 12B Instruction-Tuned (IT) model.
No additional safety layers, refusals, or alignment constraints have been intentionally added beyond those present in the base model.

The goal is to provide:

Greater freedom in system prompt design
Fewer artificial refusals
Strong general reasoning and instruction adherence
Full user control in local or private deployments

Key Features & Capabilities

Text Model (LLM)

High-quality instruction following
Strong logical and analytical reasoning
Coding assistance across multiple programming languages
Conversational and assistant-style interactions
Suitable for agentic and tool-augmented workflows

Vision-Language Model (VLM)

Image understanding and description
Visual question answering (VQA)
Image + text instruction following
Multimodal chat and assistant use cases

Chat Template & System Prompt

The model follows the Gemma instruction format.

Example:


<bos><start_of_turn>system
You are a helpful AI assistant.
<end_of_turn>
<start_of_turn>user
{your prompt here}
<end_of_turn>
<start_of_turn>assistant

For the VLM variant, images must be provided using the multimodal input format supported by your inference framework.

Intended Use Cases

General-purpose assistant — reasoning, writing, and conversation
Coding assistant — generation, debugging, and refactoring
Research & analysis — structured reasoning and synthesis
Agentic workflows — tool use, planners, function calling
Multimodal applications (VLM) — image QA, captioning, visual reasoning
Local & private deployment — full control over data and prompts

License & Usage Notes

This model inherits the Gemma License from its base model (google/gemma-3-12b-it).

The Gemma License is a custom license provided by Google
You must review and comply with the Gemma terms of use before downloading, using, or redistributing this model
This repository does not relicense the model under Apache-2.0, MIT, or any other standard open-source license

Users are solely responsible for ensuring their use complies with the Gemma License and all applicable laws and regulations.

Acknowledgements

Google for the Gemma 3 architecture and base model
BrainDAO for curation and release
The open-source community supporting local inference, quantization, and deployment tools

Community & Support

Use the Hugging Face Discussions tab for questions and updates
Community feedback and contributions are welcome

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GGUF

Model size

12B params

Architecture

gemma3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Andycurrent/gemma-3-12b-it-uncensored-GGUF

Base model

google/gemma-3-12b-pt

Finetuned

google/gemma-3-12b-it

Quantized

(150)

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UNCENSORED MODELS

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