Instructions to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M 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 majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_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 majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Use Docker

docker model run hf.co/majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M

LM Studio
Jan

vLLM

How to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Ollama
How to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M with Ollama:
```
ollama run hf.co/majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```
Unsloth Desktop
Docker Model Runner
How to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M with Docker Model Runner:
```
docker model run hf.co/majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M
```

Lemonade

How to use majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M:Q3_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-RotorQuant-GGUF-Q3_K_M-Q3_K_M

List all available models

lemonade list

Atomic Chat

KV-cache quantization (upstream, no fork needed): llama.cpp/Ollama cover this natively — -ctk q8_0 -ctv q8_0 (~~half KV memory, negligible quality loss) or -ctk q4_0 -ctv q4_0 (~~quarter memory, small quality cost). In Ollama: OLLAMA_KV_CACHE_TYPE=q8_0 with OLLAMA_FLASH_ATTENTION=1.

gemma-4-12B — RotorQuant GGUF Q3_K_M

google/gemma-4-12B @ 023679ed352de9bb66cc873c9009ce3482585c08 quantized pack, published as majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M.

Method

llama.cpp Q3_K_M quantization, text-only (vision/audio not included).

Release line

Released under the RotorQuant line. RotorQuant and TurboQuant are this project's release labels for this pack, not distinct quantization algorithms — both brand repos for a given tier carry byte-identical weights, produced once and published under two names. No brand-specific speedup is claimed or measured for either label.

Modality

TEXT-ONLY. This is a llama.cpp GGUF conversion of the text tower only — the vision and audio towers are NOT included in this pack. Do not expect image or audio understanding from this repo; use one of the MLX packs for multimodal input.

License

Governed by the Gemma Terms of Use. See the upstream repo for the full license text.

Downloads last month: 36

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

3-bit

Model tree for majentik/gemma-4-12B-RotorQuant-GGUF-Q3_K_M

Base model

google/gemma-4-12B

Quantized

(50)

this model