Instructions to use unsloth/gemma-4-12b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use unsloth/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="unsloth/gemma-4-12b-it-GGUF", filename="MTP/gemma-4-12B-it-MTP-BF16.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 unsloth/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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/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 "unsloth/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": "unsloth/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/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
- Ollama
How to use unsloth/gemma-4-12b-it-GGUF with Ollama:
ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/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 unsloth/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 unsloth/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 unsloth/gemma-4-12b-it-GGUF to start chatting
- Pi
How to use unsloth/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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
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": "unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
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 unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Docker Model Runner
How to use unsloth/gemma-4-12b-it-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/gemma-4-12b-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.gemma-4-12b-it-GGUF-UD-Q4_K_XL
List all available models
lemonade list
Please add multimodal projections
The mmproj* files seem to be missing (Or are they omitted on purpose)?
We're working on it!
We're working on it!
I'm confused. Isn't the whole point of the new unified architecture that the mmproj is not needed anymore?
There are not separate encoders for vision / audio, so the mmproj will be very small but there are still specific tensors that are deployed via mmproj. See https://huggingface.co/ggml-org/gemma-4-12B-it-GGUF/tree/main
Okay unsloth mmproj is uploaded, these are also hanging on load for me π€
"unknown projector type: gemma4uv"... wait for llama.cpp update?
Support is now fully in llama.cpp but naturally you need to be running the correct version, the just pushed a fix for the long model loading
Vision (images) works great, no issues. I'm using Gemma 4 12B Q6_K_XL and Q8_0 with the BF16 mmproj file.
I'm having some issues getting audio (mp3s) working. I'm using llama.cpp version 9496 (94a220cd6) (just rebuilt). I got one file working, it is about 1.4mb in size. I tried 3 other mp3 files over 2mb and all are giving me a really weird output of "<|channel>thought" repeating. These .mp3 files work properly on a different (older) llama.cpp instance with Gemma 4 E4B.
I can confirm a similar issue with audio (mp3s) in the current Gemma 4 12B GGUF when running through llama.cpp(b9505).
The model works normally if I do not enable --jinja, but when --jinja is enabled, the output enters a repeated loop of:
<|channel|>thought
<|channel|>thought
<|channel|>thought
...
So this looks like a chat template / special token / EOG metadata issue rather than a general model loading failure.
Current workaround:
Do not enable --jinja for now.
Observed status:
Text generation: works
MTP draft: works
Image/mmproj: loads and responds
Audio: works
Jinja template: triggers <|channel|>thought loop
It may be worth checking whether the embedded chat template, control tokens, and EOG tokens are correctly exported in the GGUF metadata.