How to use dimasjt/gemma-3n-E4b-it-Q4_K_M with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M # Run inference directly in the terminal: llama cli -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
# 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 dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
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 dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
docker model run hf.co/dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
How to use dimasjt/gemma-3n-E4b-it-Q4_K_M with Ollama:
ollama run hf.co/dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
How to use dimasjt/gemma-3n-E4b-it-Q4_K_M with Docker Model Runner:
How to use dimasjt/gemma-3n-E4b-it-Q4_K_M with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dimasjt/gemma-3n-E4b-it-Q4_K_M:Q4_K_M
lemonade run user.gemma-3n-E4b-it-Q4_K_M-Q4_K_M
lemonade list
base_model: - google/gemma-3n-E4B-it
