How to use shibatch/tinygemma4text3m with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/tinygemma4text3m", device_map="auto")
How to use shibatch/tinygemma4text3m with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf shibatch/tinygemma4text3m:Q4_K_M # Run inference directly in the terminal: llama cli -hf shibatch/tinygemma4text3m:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf shibatch/tinygemma4text3m:Q4_K_M # Run inference directly in the terminal: llama cli -hf shibatch/tinygemma4text3m: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 shibatch/tinygemma4text3m:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf shibatch/tinygemma4text3m: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 shibatch/tinygemma4text3m:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf shibatch/tinygemma4text3m:Q4_K_M
docker model run hf.co/shibatch/tinygemma4text3m:Q4_K_M
How to use shibatch/tinygemma4text3m with Ollama:
ollama run hf.co/shibatch/tinygemma4text3m:Q4_K_M
How to use shibatch/tinygemma4text3m with Docker Model Runner:
How to use shibatch/tinygemma4text3m with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shibatch/tinygemma4text3m:Q4_K_M
lemonade run user.tinygemma4text3m-Q4_K_M
lemonade list
