Instructions to use s3dev-ai/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 s3dev-ai/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 s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf s3dev-ai/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 s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf s3dev-ai/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 s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf s3dev-ai/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 s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
Use Docker
docker model run hf.co/s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use s3dev-ai/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 "s3dev-ai/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": "s3dev-ai/gemma-4-26B-A4B-it-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
- Ollama
How to use s3dev-ai/gemma-4-26B-A4B-it-gguf with Ollama:
ollama run hf.co/s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
- Unsloth Desktop
- Pi
How to use s3dev-ai/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 s3dev-ai/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": "s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use s3dev-ai/gemma-4-26B-A4B-it-gguf with Docker Model Runner:
docker model run hf.co/s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
- Lemonade
How to use s3dev-ai/gemma-4-26B-A4B-it-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull s3dev-ai/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 s3dev-ai/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 s3dev-ai/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 s3dev-ai/gemma-4-26B-A4B-it-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use s3dev-ai/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 s3dev-ai/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 "s3dev-ai/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"
Quant type
Hello. Could you please tell me if these are regular static quants (without iMatrix or other optimizations)?
Hello. These quantisations were created from the same BF16 conversion to GGUF. We used llama-cpp’s ‘convert_hf’ Python script for the original GGUF conversion, then their llama-quantize executable to create the quantisations. No additional changes were made to the base model, and no special conversion/quantisation arguments were used.
Thank you - that's exactly what I needed.