Instructions to use bartowski/google_gemma-4-26B-A4B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/google_gemma-4-26B-A4B-it-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/google_gemma-4-26B-A4B-it-GGUF", filename="google_gemma-4-26B-A4B-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
- llama.cpp
How to use bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/google_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 "bartowski/google_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": "bartowski/google_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/bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Ollama
How to use bartowski/google_gemma-4-26B-A4B-it-GGUF with Ollama:
ollama run hf.co/bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF to start chatting
- Pi new
How to use bartowski/google_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-server -hf bartowski/google_gemma-4-26B-A4B-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": "bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/google_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-server -hf bartowski/google_gemma-4-26B-A4B-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 bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/google_gemma-4-26B-A4B-it-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Lemonade
How to use bartowski/google_gemma-4-26B-A4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/google_gemma-4-26B-A4B-it-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.google_gemma-4-26B-A4B-it-GGUF-Q4_K_M
List all available models
lemonade list
<unused49> infinite generation after llama.cpp release b8699
Hi,
after updating to release b8699 which support attention rotation for heterogeneous iSWA (PR #21513) I'm getting infinite generation of token.
I saw that Unsloth (https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF/discussions/20) that they are updating their GGUF.
Do you plan to update your quants?
Thank you in advance
Can you give an example of a command that's failing? I updated to latest llama.cpp and ran the Q4_K_M I just downloaded and have no issues
Thank you for answering, I found out that the reason is V cache quantization to Q8_0.
I was using for K cache BF16 and Q8_0 for V cache and is giving error after upgrade to b8699 with your model and also with Unsloth new ones.
Using BF16 for both is working correctly as before so probably a regression on llama.cpp.
It would be great if we could figure this out. the cache quantization really helps with vram usage.
It would be an upstream issue sadly and not related to the model quantization itself :(
Yeah fair enough.