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-Q6_K.gguf (version GGUF V3 (latest)) | |
| llama_model_loader: - type f32: 339 tensors | |
| llama_model_loader: - type f16: 32 tensors | |
| llama_model_loader: - type q5_1: 8 tensors | |
| llama_model_loader: - type q8_0: 91 tensors | |
| llama_model_loader: - type q5_K: 2 tensors | |
| llama_model_loader: - type q6_K: 248 tensors | |
| print_info: file format = GGUF V3 (latest) | |
| print_info: file type = Q6_K | |
| print_info: file size = 5.69 GiB (6.50 BPW) | |
| ====== Perplexity statistics ====== | |
| Mean PPL(Q) : 57.395025 ± 0.709393 | |
| Mean PPL(base) : 52.693235 ± 0.610088 | |
| Cor(ln(PPL(Q)), ln(PPL(base))): 98.51% | |
| Mean ln(PPL(Q)/PPL(base)) : 0.085471 ± 0.002205 | |
| Mean PPL(Q)/PPL(base) : 1.089229 ± 0.002402 | |
| Mean PPL(Q)-PPL(base) : 4.701791 ± 0.150722 | |
| ====== KL divergence statistics ====== | |
| Mean KLD: 0.006872 ± 0.000100 | |
| Maximum KLD: 3.890319 | |
| 99.9% KLD: 0.332454 | |
| 99.0% KLD: 0.064667 | |
| 95.0% KLD: 0.022811 | |
| 90.0% KLD: 0.013986 | |
| Median KLD: 0.002055 | |
| 10.0% KLD: 0.000015 | |
| 5.0% KLD: 0.000002 | |
| 1.0% KLD: -0.000000 | |
| 0.1% KLD: -0.000002 | |
| Minimum KLD: -0.000105 | |
| ====== Token probability statistics ====== | |
| Mean Δp: -0.077 ± 0.006 % | |
| Maximum Δp: 75.114% | |
| 99.9% Δp: 15.320% | |
| 99.0% Δp: 6.360% | |
| 95.0% Δp: 2.144% | |
| 90.0% Δp: 0.862% | |
| 75.0% Δp: 0.043% | |
| Median Δp: -0.000% | |
| 25.0% Δp: -0.097% | |
| 10.0% Δp: -1.173% | |
| 5.0% Δp: -2.602% | |
| 1.0% Δp: -6.889% | |
| 0.1% Δp: -17.803% | |
| Minimum Δp: -88.628% | |
| RMS Δp : 2.229 ± 0.037 % | |
| Same top p: 96.672 ± 0.047 % | |