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

Libraries

How to use liodon-ai/gemma-4-12B-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="liodon-ai/gemma-4-12B-it-GGUF",
	filename="gemma4-12B-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use liodon-ai/gemma-4-12B-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/gemma-4-12B-it-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": "liodon-ai/gemma-4-12B-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

Ollama
How to use liodon-ai/gemma-4-12B-it-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
```

Unsloth Studio

How to use liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use liodon-ai/gemma-4-12B-it-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/gemma-4-12B-it-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": "liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-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 liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use liodon-ai/gemma-4-12B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/gemma-4-12B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-it-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 4 12B IT — GGUF Quantizations

GGUF quantizations of google/gemma-4-12B-it by Google DeepMind.

Model Overview

Gemma 4 12B IT is a 12-billion parameter instruction-tuned model from Google DeepMind, built on the Gemma 4 architecture. It features:

Reasoning — Configurable thinking modes for step-by-step problem solving
Coding — Strong code generation, completion, and correction capabilities
Long Context — 256K token context window
Multilingual — Support for 140+ languages
Function Calling — Native tool use support for agentic workflows
License — Apache 2.0 (free to use, modify, and redistribute)

These GGUF quantizations enable running the model locally on consumer hardware using llama.cpp, Ollama, LM Studio, and other compatible tools.

Quick Start

Ollama

ollama run hf.co/liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

llama.cpp

# Install llama.cpp
brew install llama.cpp  # macOS
# or download from https://github.com/ggerganov/llama.cpp/releases

# Start server with web UI
llama-server -hf liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

# Or run directly in terminal
llama-cli -hf liodon-ai/gemma-4-12B-it-GGUF:Q4_K_M

LM Studio

Open LM Studio
Search for liodon-ai/gemma-4-12B-it-GGUF
Download your preferred quantization
Start chatting

Jan

Open Jan
Navigate to Hub
Search liodon-ai/gemma-4-12B-it-GGUF
Download and run

Available Quantizations

Quant	File Size	Quality	Best For
`Q2_K`	~4.8 GB	Lowest — usable	Ultra-low VRAM (6GB), testing
`Q3_K_M`	~6.1 GB	Good — much better than Q2	8GB VRAM GPUs
`Q4_K_M`	~7.4 GB	Sweet spot (recommended)	8-12GB VRAM, best balance
`Q5_K_M`	~8.6 GB	High quality	12GB VRAM, near-lossless
`Q6_K`	~9.8 GB	Near-lossless	16GB VRAM, high fidelity
`Q8_0`	~12.7 GB	Basically full quality	24GB VRAM, maximum quality

Hardware Requirements

Estimated VRAM requirements for full model loading (no context):

VRAM	Q2_K	Q3_K_M	Q4_K_M	Q5_K_M	Q6_K	Q8_0
6 GB	✓	—	—	—	—	—
8 GB	✓	✓	tight	—	—	—
12 GB	✓	✓	✓	tight	—	—
16 GB	✓	✓	✓	✓	tight	—
24 GB	✓	✓	✓	✓	✓	✓

Tip: Use --cache-type-k q4_0 --cache-type-v q4_0 in llama.cpp to roughly double your available context length.

Context Length Cheat Sheet

Rough estimates for max context length at each quantization (assumes q8_0 KV cache + ~1.5 GB overhead):

VRAM	Q2_K	Q3_K_M	Q4_K_M	Q5_K_M	Q6_K	Q8_0
8 GB	~16K	~10K	~2-4K	—	—	—
12 GB	~48K	~38K	~30K	~20K	~12K	—
16 GB	~80K	~72K	~64K	~52K	~44K	~22K
24 GB	~200K	~160K	~128K	~110K	~90K	~60K
32 GB	256K (max)	256K	256K	256K	~230K	~190K

Recommended Sampling Parameters

For best results with Gemma 4:

Mode	Temperature	Top P	Top K	Use Case
General	1.0	0.95	64	Chat, creative tasks
Coding	0.6	0.95	20	Code generation
Deterministic	0.0	1.0	1	Reproducible outputs
Reasoning	1.0	0.95	64	Math, logic puzzles

Thinking Mode

Gemma 4 supports configurable thinking modes. Enable thinking for complex tasks:

Enable thinking: Add <|think|> token at the start of the system prompt
Disable thinking: Remove the <|think|> token
Default: Most libraries handle this automatically via the chat template

Model Architecture

Property	Value
Architecture	Gemma4Unified
Parameters	12B
Layers	48
Hidden Size	3,840
Attention Heads	16 (Q) / 8 (KV)
Context Length	256K tokens
Vocabulary	262,144
Sliding Window	1,024 tokens

Base Model

Model: google/gemma-4-12B-it
Organization: Google DeepMind
License: Apache 2.0
Blog: Gemma 4 Launch

Quantization Method

Quantized using llama.cpp's llama-quantize tool with the following methods:

Q2_K — 2-bit K-quants (super-block size 16)
Q3_K_M — 3-bit K-quants (medium quality)
Q4_K_M — 4-bit K-quants (medium quality, recommended)
Q5_K_M — 5-bit K-quants (medium quality)
Q6_K — 6-bit K-quants (all tensors quantized to 6-bit)
Q8_0 — 8-bit block-wise quantization (near-lossless)

Citation

@misc{gemma4_12b_it_gguf,
  title = {Gemma 4 12B IT GGUF Quantizations},
  author = {{liodon-ai}},
  year = {2026},
  url = {https://huggingface.co/liodon-ai/gemma-4-12B-it-GGUF},
  note = {Quantizations of google/gemma-4-12B-it by Google DeepMind}
}

Downloads last month: 430

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for liodon-ai/gemma-4-12B-it-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(197)

this model