Instructions to use Edmon02/gemma-4-12B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edmon02/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="Edmon02/gemma-4-12B-it-GGUF",
	filename="gemma-4-12B-it-IQ2_M.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 Edmon02/gemma-4-12B-it-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Edmon02/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-server -hf Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Edmon02/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 Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Edmon02/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 Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Edmon02/gemma-4-12B-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Edmon02/gemma-4-12B-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Edmon02/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 "Edmon02/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": "Edmon02/gemma-4-12B-it-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/Edmon02/gemma-4-12B-it-GGUF:Q4_K_M

Ollama
How to use Edmon02/gemma-4-12B-it-GGUF with Ollama:
```
ollama run hf.co/Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
```

Unsloth Studio

How to use Edmon02/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 Edmon02/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 Edmon02/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 Edmon02/gemma-4-12B-it-GGUF to start chatting

How to use Edmon02/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-server -hf Edmon02/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": "Edmon02/gemma-4-12B-it-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Edmon02/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-server -hf Edmon02/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 Edmon02/gemma-4-12B-it-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Edmon02/gemma-4-12B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/Edmon02/gemma-4-12B-it-GGUF:Q4_K_M
```

Lemonade

How to use Edmon02/gemma-4-12B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Edmon02/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 Instruction-Tuned — GGUF (multimodal)

Community GGUF mirror of google/gemma-4-12B-it for local, encoder-free multimodal AI on consumer hardware (~16 GB VRAM).

Announced June 2026: Google blog · Developer guide


Parameters	~12B dense
Modalities	Text, vision, audio (native in backbone)
License	Apache 2.0
Architecture	Encoder-free (no separate vision/audio towers)
Context	See upstream config
Vision in GGUF	Requires *`mmproj-.gguf`** alongside main weights

Why this repo exists

One download hub for all major quants (K-quants, IQ, Q8, mmproj).
Fast Hub-side sync from bartowski/gemma-4-12B-it-GGUF — no re-upload from your laptop.
Documented use cases for contributors: gemma-4-12b-local (agents, LiteRT, llama.cpp, MLX).

Available files

See gguf-manifest.json for the live file list.

Essential tier (recommended)

File	Use
`gemma-4-12B-it-Q4_K_M.gguf`	Best balance — 16 GB laptops
`gemma-4-12B-it-Q5_K_M.gguf`	Higher quality
`gemma-4-12B-it-Q6_K.gguf` / `Q8_0`	Max quality
`gemma-4-12B-it-Q3_K_M.gguf`	Tighter VRAM
`gemma-4-12B-it-Q2_K.gguf`	Minimum size
`gemma-4-12B-it-IQ4_XS.gguf` / `IQ4_NL.gguf`	IQ variants
`mmproj-gemma-4-12B-it-f16.gguf`	Required for images in llama.cpp

Full tier

All bartowski quants (Q2_K_L, Q3_K_XL, Q4_0, Q4_1, bf16, imatrix, etc.) — run make sync-gemma4-gguf-full.

Download

pip install -U huggingface_hub

# Text + vision (recommended)
huggingface-cli download Edmon02/gemma-4-12B-it-GGUF \
  gemma-4-12B-it-Q4_K_M.gguf \
  mmproj-gemma-4-12B-it-f16.gguf \
  --local-dir ./models/gemma-4-12b

Accept the license on google/gemma-4-12B-it before using weights.

Quick start

llama.cpp (text)

llama-cli -m gemma-4-12B-it-Q4_K_M.gguf -p "Explain encoder-free multimodal models in 3 bullets." -n 256

llama.cpp (image + text)

llama-mtmd-cli \
  -m gemma-4-12B-it-Q4_K_M.gguf \
  --mmproj mmproj-gemma-4-12B-it-f16.gguf \
  --image photo.jpg \
  -p "Describe this image."

LiteRT-LM (OpenAI-compatible local server)

litert-lm import --from-huggingface-repo=litert-community/gemma-4-12B-it-litert-lm gemma-4-12B-it.litertlm gemma4-12b
litert-lm serve

LM Studio / Ollama

Import Edmon02/gemma-4-12B-it-GGUF and select Q4_K_M + mmproj.

Use cases

Use case	Quant	Tool
Local coding agent	Q4_K_M	OpenCode, Continue, Aider
Voice + vision assistant	Q5_K_M + mmproj	Google AI Edge Gallery / Eloquent (Mac)
Armenian + English research	Q4_K_M	Pair with HyVoxPopuli ASR/TTS
Low-VRAM laptop	Q3_K_M or IQ4_XS	llama.cpp
Fast inference	MTP drafter (upstream)	Google checkpoint + compatible runtime

Hardware guide

VRAM	Suggested files
8 GB	IQ4_XS or Q3_K_M (text only, short context)
16 GB	Q4_K_M + mmproj
24 GB+	Q6_K or Q8_0 + mmproj

Provenance

Item	Source
Base model	`google/gemma-4-12B-it`
GGUF quants	Mirrored from `bartowski/gemma-4-12B-it-GGUF`
Maintainer scripts	Edmon02/audio_set `scripts/sync_gemma4_gguf_quants.py`

Limitations

Community quants — validate quality on your tasks vs official BF16.
Audio in GGUF may require latest llama.cpp / LM Studio builds.
Gated upstream — HF token + license acceptance required for google/* repos.

Contributing

Add recipes under projects/gemma-4-12b-local/examples/. See CONTRIBUTING.md in that folder.

Citation

@article{gemma_2026,
  title={Gemma 4},
  author={Google DeepMind},
  year={2026},
  url={https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12B/}
}

Downloads last month: 5,028

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

View +1 variant

Model tree for Edmon02/gemma-4-12B-it-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

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