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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
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

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`	MLX FP16	`mlx-vlm` ready
`gemma4-e2b-Snowfox-MLX-4bit`	MLX 4-bit affine	~3.55 GB
`gemma4-e2b-Snowfox-MLX-6bit`	MLX 6-bit affine	~4.71 GB
`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 and NOTICE.md for the lineage and modification notice.

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Safetensors

Model size

5B params

Tensor type

BF16

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

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

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

Finetuned

(21)

this model

Quantizations

1 model