Instructions to use giladgd/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 giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf giladgd/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 serve -hf giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
Use Docker
docker model run hf.co/giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use giladgd/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 "giladgd/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": "giladgd/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/giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Ollama
How to use giladgd/gemma-4-26B-A4B-it-GGUF with Ollama:
ollama run hf.co/giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF: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": "giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use giladgd/gemma-4-26B-A4B-it-GGUF with Docker Model Runner:
docker model run hf.co/giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
- Lemonade
How to use giladgd/gemma-4-26B-A4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use giladgd/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 giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use giladgd/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 giladgd/gemma-4-26B-A4B-it-GGUF: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 "giladgd/gemma-4-26B-A4B-it-GGUF: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"
gemma-4-26B-A4B-it-GGUF
Read our blog post to learn more about using Gemma 4 with
node-llama-cpp
Static quants of google/gemma-4-26B-A4B-it.
Quants
| Link | URI | Quant | Size |
|---|---|---|---|
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q2_K |
Q2_K | 10.6GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q3_K_S |
Q3_K_S | 12.2GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q3_K_M |
Q3_K_M | 13.3GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q3_K_L |
Q3_K_L | 13.8GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q4_0 |
Q4_0 | 14.4GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_S |
Q4_K_S | 15.5GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M |
Q4_K_M | 16.8GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q5_0 |
Q5_0 | 17.5GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q5_K_S |
Q5_K_S | 18.0GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q5_K_M |
Q5_K_M | 19.1GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q6_K |
Q6_K | 22.6GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q8_0 |
Q8_0 | 26.9GB |
| GGUF | hf:giladgd/gemma-4-26B-A4B-it-GGUF:BF16 |
BF16 | 50.5GB |
Download a quant using
node-llama-cpp(more info):npx -y node-llama-cpp pull <URI>
Usage
Use with node-llama-cpp (recommended)
Ensure you have node.js installed:
brew install nodejs
CLI
Chat with the model:
npx -y node-llama-cpp chat hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M
Code
Use it in your project:
npm install node-llama-cpp
import {getLlama, resolveModelFile, LlamaChatSession} from "node-llama-cpp";
const modelUri = "hf:giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M";
const llama = await getLlama();
const model = await llama.loadModel({
modelPath: await resolveModelFile(modelUri)
});
const context = await model.createContext();
const session = new LlamaChatSession({
contextSequence: context.getSequence()
});
const q1 = "Hi there, how are you?";
console.log("User: " + q1);
const a1 = await session.prompt(q1);
console.log("AI: " + a1);
Read the getting started guide to quickly scaffold a new
node-llama-cppproject
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
CLI
llama-cli -hf giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M -p "The meaning to life and the universe is"
Server
llama-server -hf giladgd/gemma-4-26B-A4B-it-GGUF:Q4_K_M -c 2048
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