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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.
|