Instructions to use MichaelAnthony/gemma4-e2b-Snowfox-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 MichaelAnthony/gemma4-e2b-Snowfox-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 MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

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 MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

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 MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with Ollama:
```
ollama run hf.co/MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
```

Unsloth Studio

How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MichaelAnthony/gemma4-e2b-Snowfox-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MichaelAnthony/gemma4-e2b-Snowfox-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MichaelAnthony/gemma4-e2b-Snowfox-GGUF to start chatting

How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

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 "MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with Docker Model Runner:
```
docker model run hf.co/MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M
```

Lemonade

How to use MichaelAnthony/gemma4-e2b-Snowfox-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma4-e2b-Snowfox-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use MichaelAnthony/gemma4-e2b-Snowfox-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 MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

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 MichaelAnthony/gemma4-e2b-Snowfox-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Gemma 4 E2B SnowFox GGUF

This is the GGUF distribution of gemma4-e2b-Snowfox: a language-only SnowFox LoRA merge based on Google's Gemma 4 E2B instruction QAT-derived checkpoint. The image and audio towers were frozen during SnowFox fine-tuning. The post-merge weights were exported to the four GGUF quantizations below; they were not separately re-QAT calibrated.

Download exactly one LLM quantization, plus the shared projector

LLM file	Size	SHA-256
`gemma4-e2b-Snowfox-Q4_0.gguf`	3,349,516,000 bytes	`105e63469457449dcdc147785c4317b7891a9aafaec2e40bdcbc1b16c6fd8f3b`
`gemma4-e2b-Snowfox-Q4_K_M.gguf`	3,416,120,032 bytes	`f4a38a2a95cefbf91659c355aa80410c9e0b307072c3dc7deca842b2d76d613a`
`gemma4-e2b-Snowfox-Q6_K.gguf`	3,829,835,488 bytes	`e7d2a0c8cc168db9652799d3bfd8198563dc821bae424b4b8ed1a767bbc51537`
`gemma4-e2b-Snowfox-Q8_0.gguf`	4,947,414,752 bytes	`44e7f9dd879f0ae2b18f57f78819e147783b4795738b3d3321f6e20542fa6a68`

For a multimodal-capable llama.cpp-family runtime, also download the shared projector:

Projector file	Size	SHA-256
`gemma4-e2b-Snowfox-BF16-mmproj.gguf`	986,833,408 bytes	`cbc8946d8e6a85714c7f4868302ccd7bf716301b47a5efefe5850eb5348b0a3e`

Q4_K_M is the usual balanced default. Use a recent runtime that supports Gemma 4 E2B and its multimodal projector. This repository intentionally holds all four verified downloadable quantizations in one GGUF-only package.

Example download:

hf download MichaelAnthony/gemma4-e2b-Snowfox-GGUF \
  gemma4-e2b-Snowfox-Q4_K_M.gguf \
  gemma4-e2b-Snowfox-BF16-mmproj.gguf \
  --local-dir ./gemma4-e2b-Snowfox-GGUF

Lineage and validation

Base: google/gemma-4-E2B-it-qat-q4_0-unquantized
Pinned base revision: 6befbaca7398925921802abd1f277b495b78b738
Release claim: QAT-derived; the SnowFox post-LoRA weights were not newly QAT-calibrated.
Internal release gates: required-field accuracy 0.9984962406015038, strict JSON validity 1.0, native tool-call validity 1.0, and contract success 0.9936305732484076.

Those figures are the original held-out SnowFox validation results, not an inference benchmark for every GGUF/runtime combination.

License

Gemma 4 is Apache-2.0. This derivative package uses the Apache-2.0 license declared by the pinned base model. See LICENSE and NOTICE.md for the lineage and modification notice.

Downloads last month: -

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

6-bit

8-bit

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Model tree for MichaelAnthony/gemma4-e2b-Snowfox-GGUF

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

google/gemma-4-E2B-it-qat-q4_0-unquantized

Quantized

(42)

this model