Image-Text-to-Text
MLX
Safetensors
gemma4
mlx-vlm
4-bit precision
quantized
affine
snowfox
conversational
Instructions to use MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit") config = load_config("MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit"
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-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit"
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-MLX-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload README.md with huggingface_hub
Browse files
README.md
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- `config.json` (with `quantization` + `quantization_config`), `generation_config.json`,
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`processor_config.json`, tokenizer files, and `chat_template.jinja`.
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## Verification performed
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The conversion host has no Apple-Silicon MLX runtime, so the quantized package
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- `config.json` (with `quantization` + `quantization_config`), `generation_config.json`,
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`processor_config.json`, tokenizer files, and `chat_template.jinja`.
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## Model size vs HF parameter display
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This is a **~5.1B-parameter** model (2.3B effective), identical to the source
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SnowFox checkpoint. Hugging Face's model page reports ~1.2B because the 4-bit
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weights are stored as *packed* `uint32` words (8 values each) and HF counts each
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packed word as one parameter. The packed word count is a storage detail, not the
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parameter count.
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## Verification performed
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The conversion host has no Apple-Silicon MLX runtime, so the quantized package
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