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

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

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"

gemma-4-E4B-it-GGUF / scores /gemma-4-E4B-it-q2_k.hsw

eaddario

Generate Perplexity, KLD, ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores

2c7e1e2 verified 4 months ago

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1.06 kB

	llama_model_loader: loaded meta data with 49 key-value pairs and 720 tensors from gemma-4-E4B-it-WIP/gemma-4-E4B-it-Q2_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: 7 tensors
	llama_model_loader: - type q2_K: 112 tensors
	llama_model_loader: - type q3_K: 36 tensors
	llama_model_loader: - type q4_K: 15 tensors
	llama_model_loader: - type q5_K: 2 tensors
	llama_model_loader: - type iq2_xxs: 94 tensors
	llama_model_loader: - type iq2_xs: 38 tensors
	llama_model_loader: - type iq3_xxs: 38 tensors
	llama_model_loader: - type iq4_xs: 2 tensors
	llama_model_loader: - type iq1_m: 36 tensors
	print_info: file format = GGUF V3 (latest)
	print_info: file type = IQ2_S - 2.5 bpw
	print_info: file size = 2.19 GiB (2.50 BPW)

	hellaswag_score : loaded 10042 tasks from prompt.
	hellaswag_score : selecting 750 randomized tasks.
	hellaswag_score : calculating hellaswag score over selected tasks.

	750 42.53333333% [39.0419%, 46.1008%]