Instructions to use MichaelAnthony/gemma4-e2b-Snowfox-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX 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")
config = load_config("MichaelAnthony/gemma4-e2b-Snowfox-MLX")

# 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

How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX 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"

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"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX 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"

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MichaelAnthony/gemma4-e2b-Snowfox-MLX 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"

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" \
  --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"

gemma4-e2b-Snowfox-MLX / README.md

MichaelAnthony

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	---
	base_model: google/gemma-4-E2B-it-qat-q4_0-unquantized
	library_name: mlx
	pipeline_tag: image-text-to-text
	license: apache-2.0
	tags:
	- gemma4
	- mlx
	- mlx-vlm
	- fp16
	- qat-derived
	- snowfox
	---

	# Gemma 4 E2B SnowFox MLX FP16

	This repository contains exactly one MLX variant: the unquantized FP16
	SnowFox model. It is a genuine MLX safetensors package, not a GGUF file or a
	renamed Hugging Face BF16 checkpoint. Four safetensors files make up one model;
	the shard split is only for reliable large-file download.

	SnowFox is a language-only LoRA merge based on Google's Gemma 4 E2B instruction
	QAT-derived checkpoint. The image and audio towers were frozen during fine-tuning
	and are retained here, together with the processor and tokenizer needed by
	MLX-VLM.

	## Exact lineage

	- Base: [`google/gemma-4-E2B-it-qat-q4_0-unquantized`](https://huggingface.co/google/gemma-4-E2B-it-qat-q4_0-unquantized)
	- Pinned base revision: `6befbaca7398925921802abd1f277b495b78b738`
	- Canonical merged BF16 source SHA-256: `b8fac0ad2cafcb0e7fe29ca6c1deda1389c645751599fe716d4b6f6c0387a2d5`
	- Conversion: structurally converted to the MLX-VLM v0.6.13 Gemma 4 tensor contract, then cast from BF16 to FP16 for storage.
	- Claim boundary: QAT-derived from the base; SnowFox was not trained in FP16 and the post-LoRA weights were not newly QAT-calibrated.

	## Package contents

	- `model-00001-of-00004.safetensors` through `model-00004-of-00004.safetensors`: the one FP16 MLX model.
	- `model.safetensors.index.json`: complete shard map.
	- `config.json`, `generation_config.json`, `processor_config.json`, tokenizer files, and `chat_template.jinja`: Gemma 4 E2B multimodal support files.
	- `mlx_export_manifest.json`: source/output provenance and artifact hashes.

	## Verification performed

	The Windows conversion host does not have a compatible MLX runtime, but the
	stored model conversion was exhaustively verified before upload:

	- 1,951 source tensors mapped to 1,951 MLX tensors with no missing or extra keys.
	- All 5,104,298,467 stored values were checked after conversion.
	- Every output tensor is finite FP16, has exact BF16-to-FP16 values, and its
	safetensors shard declares `format=mlx`.
	- The largest absolute stored weight is `900.0`, below FP16's finite limit.
	- The full image/audio/projector tensor set is present; Gemma 4 audio convolution
	weights use the MLX-VLM axis layout.

	**Apple-Silicon MLX-VLM inference has not been run from this Windows/AMD release
	host.** Treat this as structurally validated conversion data pending a real
	Apple-Silicon text, image, and audio generation smoke test; do not interpret the
	SnowFox training validation scores as fresh MLX runtime results.

	## Run on Apple Silicon

	Use full MLX-VLM, not text-only MLX-LM, because Gemma 4 E2B includes image and
	audio components:

	```bash
	python -m pip install "mlx-vlm==0.6.13"

	python -m mlx_vlm.generate \
	--model MichaelAnthony/gemma4-e2b-Snowfox-MLX \
	--max-tokens 128 \
	--temperature 0.0 \
	--prompt "Explain what SnowFox is in one sentence."
	```

	For image prompting, add `--image /path/to/image.png` to the generation command.
	Use current MLX-VLM documentation for image, audio, video, and chat-template
	options.

	## Quantized variants

	Standard MLX-VLM affine quantizations of SnowFox are published as separate
	repositories and are loadable directly by `mlx_vlm.generate`:

	\| Variant \| Quantization \| Size \| Notes \|
	\| --- \| --- \| --- \| --- \|
	\| [`gemma4-e2b-Snowfox-MLX-4bit`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit) \| 4-bit affine, group 64 \| ~3.55 GB \| GGUF `Q4_K_M` analogue \|
	\| [`gemma4-e2b-Snowfox-MLX-6bit`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-MLX-6bit) \| 6-bit affine, group 64 \| ~4.71 GB \| GGUF `Q6_K` analogue \|

	These quantize the language backbone (including the large per-layer embeddings)
	to 4-bit/6-bit affine while keeping the vision and audio towers dense in FP16,
	so they are smaller than a standard Linear-only quantization.

	The earlier oMLX oQ ("oQ4/oQ6/oQ8") build-to-order plan was never published;
	use the standard 4-bit/6-bit packages above instead.

	## License

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