Instructions to use ssfdre38/gemma4-nano-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 ssfdre38/gemma4-nano-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 ssfdre38/gemma4-nano-gguf
# Run inference directly in the terminal:
llama cli -hf ssfdre38/gemma4-nano-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ssfdre38/gemma4-nano-gguf
# Run inference directly in the terminal:
llama cli -hf ssfdre38/gemma4-nano-gguf

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 ssfdre38/gemma4-nano-gguf
# Run inference directly in the terminal:
./llama-cli -hf ssfdre38/gemma4-nano-gguf

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 ssfdre38/gemma4-nano-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ssfdre38/gemma4-nano-gguf

Use Docker

docker model run hf.co/ssfdre38/gemma4-nano-gguf

LM Studio
Jan
Ollama
How to use ssfdre38/gemma4-nano-gguf with Ollama:
```
ollama run hf.co/ssfdre38/gemma4-nano-gguf
```
Unsloth Desktop

How to use ssfdre38/gemma4-nano-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ssfdre38/gemma4-nano-gguf

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": "ssfdre38/gemma4-nano-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ssfdre38/gemma4-nano-gguf with Docker Model Runner:
```
docker model run hf.co/ssfdre38/gemma4-nano-gguf
```

Lemonade

How to use ssfdre38/gemma4-nano-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ssfdre38/gemma4-nano-gguf

Run and chat with the model

lemonade run user.gemma4-nano-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ssfdre38/gemma4-nano-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 ssfdre38/gemma4-nano-gguf

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 ssfdre38/gemma4-nano-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ssfdre38/gemma4-nano-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ssfdre38/gemma4-nano-gguf

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 "ssfdre38/gemma4-nano-gguf" \
  --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"

gemma4-nano GGUF Files

Ultra-compressed Gemma 4 models optimized for mobile and edge devices.

Quick Start with Ollama

ollama run ssfdre38/gemma4-nano:e2b   # 3.1 GB
ollama run ssfdre38/gemma4-nano:e4b   # 4.7 GB (latest)
ollama run ssfdre38/gemma4-nano:26b   # 12 GB
ollama run ssfdre38/gemma4-nano:31b   # 13 GB

Or download GGUFs directly:

gemma4-e2b-q3ks-nano.gguf (2.9 GB) - Fits 4GB RAM devices
gemma4-e4b-q3ks-nano.gguf (4.3 GB) - Recommended default
gemma4-26b-q3ks-nano.gguf (11.4 GB) - High capability
gemma4-31b-q3ks-nano.gguf (12.8 GB) - 31B params, smaller than stock e4b!

Use with Ollama (from GGUF)

# Download GGUF
wget https://huggingface.co/ssfdre38/gemma4-nano-gguf/resolve/main/gemma4-e2b-q3ks-nano.gguf

# Create Modelfile
echo "FROM ./gemma4-e2b-q3ks-nano.gguf
PARAMETER num_ctx 16384" > Modelfile

# Create model
ollama create my-nano -f Modelfile
ollama run my-nano

Specifications

Quantization: Q3_K_S (3.41 bits per weight)
Context: 16,384 tokens
Format: GGUF v3
Modality: Text-only (no vision encoder)

Performance

Nano models achieve 13% faster inference than gemma4-turbo on CPU with 8 threads.

Model	Size	Reduction vs Stock	Notes
E2b nano	3.1 GB	-57% (was 7.2 GB)	Fits 4GB RAM devices
E4b nano	4.7 GB	-51% (was 9.6 GB)	Recommended default
26b nano	12 GB	-29% (was 17 GB)	High capability
31b nano	13 GB	-32% (was 19 GB)	31B params < stock e4b size!

Documentation

Related Models

ssfdre38/gemma4-turbo - IQ4_XS with vision encoder

License

Apache 2.0 (same as Gemma 4 base models)

Built for the Gemma 4 Good Hackathon 2026 🦞

Downloads last month: 1,823

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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