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q4_0-unquantized checkpoint, which carries clipping parameters on
the multimodal towers that are preserved here.| 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 packeduint32words (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).
google/gemma-4-E2B-it-qat-q4_0-unquantized6befbaca7398925921802abd1f277b495b78b7381from transformers import AutoModelForCausalLM, AutoProcessor
2
3model_id = "MichaelAnthony/gemma4-e2b-Snowfox-hf"
4processor = AutoProcessor.from_pretrained(model_id)
5model = AutoModelForCausalLM.from_pretrained(
6 model_id,
7 torch_dtype="auto",
8 device_map="auto",
9)| 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 |