license: gemma
base_model:
- google/gemma-4-26B-A4B-it
- huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated
- Jiunsong/supergemma4-26b-abliterated-multimodal
tags: - gemma4
- mlx
- multimodal
- image-text-to-text
- abliterated
- uncensored
- quantized
- 4-bit
- apple-silicon
language: - en
- ko
pipeline_tag: image-text-to-text
library_name: mlx
Support ongoing open-source work: ko-fi.com/jiunsong
SuperGemma4-26B-Abliterated-Multimodal MLX 4bit
This is the lighter-weight MLX build of Jiunsong/supergemma4-26b-abliterated-multimodal.
It preserves multimodal behavior while reducing local storage and memory demand for Apple Silicon setups that want a smaller package.
Important note on the Hugging Face size badge
If the Hub UI shows this repo as a smaller class such as 5B or 8B, that is a Hub-side auto-inference artifact from the exported MLX quantized config.
This repo is still a quantized release of the full SuperGemma4-26B-Abliterated-Multimodal line derived from the Gemma 4 26B-A4B multimodal family. The smaller badge does not mean the model was accidentally converted into a different 5B or 8B model.
Why this variant
- Smaller MLX footprint for local use
- Keeps text + vision support
- Preserves the abliterated / low-refusal behavior of the main release
- Good option when you want better fit on-device without dropping multimodality
- Verified with both text-only and image-grounded prompts
Validation
- Text check: returned
READY - Image check: returned
redfor a solid red test image - Disk footprint: about
15 GB
Recommended use
Pick this version when you want a smaller MLX package and are willing to trade a bit of precision for a lighter local deployment.
Quick start
python3 -m mlx_vlm.server \
--model /absolute/path/to/supergemma4-26b-abliterated-multimodal-mlx-4bit \
--host 127.0.0.1 \
--port 8091
from mlx_vlm import load
model, processor = load("/absolute/path/to/supergemma4-26b-abliterated-multimodal-mlx-4bit")
print("Loaded.")