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-Q3_K.gguf (version GGUF V3 (latest)) | |
| llama_model_loader: - type f32: 339 tensors | |
| llama_model_loader: - type f16: 1 tensors | |
| llama_model_loader: - type q4_1: 11 tensors | |
| llama_model_loader: - type q5_1: 6 tensors | |
| llama_model_loader: - type q8_0: 1 tensors | |
| llama_model_loader: - type q2_K: 11 tensors | |
| llama_model_loader: - type q3_K: 118 tensors | |
| llama_model_loader: - type q4_K: 41 tensors | |
| llama_model_loader: - type q5_K: 6 tensors | |
| llama_model_loader: - type iq3_xxs: 41 tensors | |
| llama_model_loader: - type iq3_s: 49 tensors | |
| llama_model_loader: - type iq4_xs: 96 tensors | |
| print_info: file format = GGUF V3 (latest) | |
| print_info: file type = IQ4_XS - 4.25 bpw | |
| print_info: file size = 3.06 GiB (3.50 BPW) | |
| ====== Perplexity statistics ====== | |
| Mean PPL(Q) : 53.460778 ± 0.637355 | |
| Mean PPL(base) : 52.693235 ± 0.610088 | |
| Cor(ln(PPL(Q)), ln(PPL(base))): 93.43% | |
| Mean ln(PPL(Q)/PPL(base)) : 0.014461 ± 0.004273 | |
| Mean PPL(Q)/PPL(base) : 1.014566 ± 0.004336 | |
| Mean PPL(Q)-PPL(base) : 0.767544 ± 0.227712 | |
| ====== KL divergence statistics ====== | |
| Mean KLD: 0.368276 ± 0.001632 | |
| Maximum KLD: 16.233898 | |
| 99.9% KLD: 6.639840 | |
| 99.0% KLD: 2.925996 | |
| 95.0% KLD: 1.350669 | |
| 90.0% KLD: 0.914818 | |
| Median KLD: 0.166417 | |
| 10.0% KLD: 0.001494 | |
| 5.0% KLD: 0.000202 | |
| 1.0% KLD: 0.000008 | |
| 0.1% KLD: 0.000000 | |
| Minimum KLD: -0.000030 | |
| ====== Token probability statistics ====== | |
| Mean Δp: -1.997 ± 0.038 % | |
| Maximum Δp: 99.669% | |
| 99.9% Δp: 75.373% | |
| 99.0% Δp: 40.206% | |
| 95.0% Δp: 14.981% | |
| 90.0% Δp: 6.041% | |
| 75.0% Δp: 0.341% | |
| Median Δp: -0.002% | |
| 25.0% Δp: -1.599% | |
| 10.0% Δp: -13.659% | |
| 5.0% Δp: -27.491% | |
| 1.0% Δp: -61.928% | |
| 0.1% Δp: -94.101% | |
| Minimum Δp: -99.998% | |
| RMS Δp : 14.827 ± 0.072 % | |
| Same top p: 75.953 ± 0.111 % | |