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

Libraries

How to use MichaelAnthony/gemma4-e2b-Snowfox-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="MichaelAnthony/gemma4-e2b-Snowfox-hf")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MichaelAnthony/gemma4-e2b-Snowfox-hf")
model = AutoModelForMultimodalLM.from_pretrained("MichaelAnthony/gemma4-e2b-Snowfox-hf", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MichaelAnthony/gemma4-e2b-Snowfox-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MichaelAnthony/gemma4-e2b-Snowfox-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MichaelAnthony/gemma4-e2b-Snowfox-hf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

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

SGLang

How to use MichaelAnthony/gemma4-e2b-Snowfox-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MichaelAnthony/gemma4-e2b-Snowfox-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MichaelAnthony/gemma4-e2b-Snowfox-hf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MichaelAnthony/gemma4-e2b-Snowfox-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MichaelAnthony/gemma4-e2b-Snowfox-hf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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

gemma4-e2b-Snowfox-hf / README.md

MichaelAnthony

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	---
	base_model: google/gemma-4-E2B-it-qat-q4_0-unquantized
	library_name: transformers
	pipeline_tag: image-text-to-text
	license: apache-2.0
	tags:
	- gemma4
	- snowfox
	- qat-derived
	- safetensors
	- transformers
	- multimodal
	- text-generation
	---

	# Gemma 4 E2B SnowFox (canonical BF16 source)

	This is the canonical merged BF16 Transformers checkpoint of SnowFox — a
	language-only LoRA merge built on Google's Gemma 4 E2B instruction QAT-derived
	model. Every SnowFox distribution (MLX FP16, MLX 4-bit, MLX 6-bit, GGUF) is
	derived from this repository, so this is the package to use for full-precision
	Transformers inference or as the source for your own exports.

	SnowFox is trained by Michael Anthony Falabella.

	## What SnowFox is

	SnowFox is a language-only LoRA merge: the image and audio towers were frozen
	during fine-tuning and are retained unchanged from the base model. Only the
	language backbone received the SnowFox LoRA adaptation. The base is Google's
	QAT-derived `q4_0-unquantized` checkpoint, which carries clipping parameters on
	the multimodal towers that are preserved here.

	## Model size

	\| Property \| Value \|
	\|---\|---\|
	\| Total parameters \| ~5.1B (with per-layer embeddings) \|
	\| Effective parameters \| ~2.3B \|
	\| Weights format \| BF16 \|
	\| Checkpoint size \| ~10.2 GB (`model.safetensors`) \|

	> Note: Hugging Face's model page may report a smaller "params" figure for the
	> quantized MLX derivatives of this model. That is a display artifact — those
	> repos store weights as packed `uint32` words (8× 4-bit / 5× 6-bit values per
	> word) and HF counts each packed word as one parameter. The true count is
	> unchanged (~5.1B total / ~2.3B effective).

	## 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`
	- Method: LoRA fine-tune (language-only), merged into the base model
	- Claim boundary: QAT-derived from the base; SnowFox's post-LoRA weights were
	not newly QAT-calibrated.

	## Quick start

	```python
	from transformers import AutoModelForCausalLM, AutoProcessor

	model_id = "MichaelAnthony/gemma4-e2b-Snowfox-hf"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	)
	```

	## Derivative packages

	\| Package \| Format \| Notes \|
	\|---\|---\|---\|
	\| [`gemma4-e2b-Snowfox-MLX`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-MLX) \| MLX FP16 \| `mlx-vlm` ready \|
	\| [`gemma4-e2b-Snowfox-MLX-4bit`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-MLX-4bit) \| MLX 4-bit affine \| ~3.55 GB \|
	\| [`gemma4-e2b-Snowfox-MLX-6bit`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-MLX-6bit) \| MLX 6-bit affine \| ~4.71 GB \|
	\| [`gemma4-e2b-Snowfox-GGUF`](https://huggingface.co/MichaelAnthony/gemma4-e2b-Snowfox-GGUF) \| GGUF \| llama.cpp / Ollama \|

	## 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.