Instructions to use unsloth/gemma-4-26B-A4B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/gemma-4-26B-A4B-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
- LM Studio
- Jan
- vLLM
How to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-Q4_K_M
- Ollama
How to use unsloth/gemma-4-26B-A4B-it-GGUF with Ollama:
ollama run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use unsloth/gemma-4-26B-A4B-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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-4-26B-A4B-it-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use unsloth/gemma-4-26B-A4B-it-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
- Lemonade
How to use unsloth/gemma-4-26B-A4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-GGUF-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use unsloth/gemma-4-26B-A4B-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use unsloth/gemma-4-26B-A4B-it-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Only 2nd <13GB model to one-shot the Heptagon-Tumbler
I tested 87 recent models under 13GB total filesize. This is only the second model to succeed [one-shot] at the Heptagon-Tumbler challenge posted 1 year ago on reddit.
gemma-4-26B-A4B-it-UD-IQ4_XS.gguf solved the heptagon-tumbler challenge:
Write a python program that shows a graphical animated rendition of 20 balls bouncing inside a spinning hollow heptagon:
All balls have the same radius.
All balls drop from the heptagon center when starting.
Colors are: #f8b862, #f6ad49, #f39800, #f08300, #ec6d51, #ee7948, #ed6d3d, #ec6800, #ec6800, #ee7800, #eb6238, #ea5506, #ea5506, #eb6101, #e49e61, #e45e32, #e17b34, #dd7a56, #db8449, #d66a35
The balls should be affected by gravity and friction, and they must be contained within the area of the heptagon by physical collision detection, making the balls bounce off the rotating walls realistically. There should also be collisions between balls.
The heptagon is spinning around its center, rotating a full cycle once every 5 seconds.
The heptagon size should be large enough to contain all the balls.
Do not use the pygame library; implement collision detection algorithms and collision response etc. by yourself. The following python libraries are allowed: tkinter, math, numpy, dataclasses, typing, sys.
All program code should be put in a single python file, with shebang for execution from bash shell.
Out of 87 tested <13GB models, this is only the second one to pass the test.
The other model was ByteShape's Qwen3-Coder-30B-A3B-Instruct-Q3_K_S-2.69bpw.gguf
Surprised and impressed at this result.
Thanks for testing and for your awesome results!