Instructions to use eaddario/gemma-4-E4B-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 eaddario/gemma-4-E4B-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 eaddario/gemma-4-E4B-it-GGUF:F16 # Run inference directly in the terminal: llama cli -hf eaddario/gemma-4-E4B-it-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf eaddario/gemma-4-E4B-it-GGUF:F16 # Run inference directly in the terminal: llama cli -hf eaddario/gemma-4-E4B-it-GGUF:F16
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 eaddario/gemma-4-E4B-it-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf eaddario/gemma-4-E4B-it-GGUF:F16
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 eaddario/gemma-4-E4B-it-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf eaddario/gemma-4-E4B-it-GGUF:F16
Use Docker
docker model run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use eaddario/gemma-4-E4B-it-GGUF with Ollama:
ollama run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16
- Unsloth Desktop
- Pi
How to use eaddario/gemma-4-E4B-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 eaddario/gemma-4-E4B-it-GGUF:F16
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": "eaddario/gemma-4-E4B-it-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use eaddario/gemma-4-E4B-it-GGUF with Docker Model Runner:
docker model run hf.co/eaddario/gemma-4-E4B-it-GGUF:F16
- Lemonade
How to use eaddario/gemma-4-E4B-it-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eaddario/gemma-4-E4B-it-GGUF:F16
Run and chat with the model
lemonade run user.gemma-4-E4B-it-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use eaddario/gemma-4-E4B-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 eaddario/gemma-4-E4B-it-GGUF:F16
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 eaddario/gemma-4-E4B-it-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use eaddario/gemma-4-E4B-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 eaddario/gemma-4-E4B-it-GGUF:F16
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 "eaddario/gemma-4-E4B-it-GGUF:F16" \ --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"
| llama_model_loader: loaded meta data with 49 key-value pairs and 720 tensors from gemma-4-E4B-it-WIP/gemma-4-E4B-it-Q8_0.gguf (version GGUF V3 (latest)) | |
| llama_model_loader: - type f32: 339 tensors | |
| llama_model_loader: - type f16: 60 tensors | |
| llama_model_loader: - type q8_0: 309 tensors | |
| llama_model_loader: - type q6_K: 12 tensors | |
| print_info: file format = GGUF V3 (latest) | |
| print_info: file type = Q8_0 | |
| print_info: file size = 7.44 GiB (8.50 BPW) | |
| multiple_choice_score: there are 869 tasks in prompt | |
| multiple_choice_score: selecting 750 random tasks from 869 tasks available | |
| multiple_choice_score : calculating TruthfulQA score over 750 tasks. | |
| Final result: 56.9333 +/- 1.8093 | |
| Random chance: 25.0083 +/- 1.5824 | |