Downloads · 30 days
1.9K
8% of all-time downloads
Jiunsong/supergemma4-26b-abliterated-multimodal-mlx-4bit
supergemma4-26b-abliterated-multimodal-mlx-4bit is a image-text-to-text model from Jiunsong. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as gemma.
Support ongoing open-source work: ko-fi.com/jiunsong
Downloads · 30 days
1.9K
8% of all-time downloads
All-time downloads
24.7K
Public
Parameters
25.8B
15.6 GB on disk
Likes
68
Public
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.safetensors15.6 GB · 100%
How the weights are stored.
U3225.2B · 98%
From the Hugging Face model README
Support ongoing open-source work: ko-fi.com/jiunsong
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.
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.
9 / 9, reliability audit 20 / 20.READYred for a solid red test image15 GBPick this version when you want a smaller MLX package and are willing to trade a bit of precision for a lighter local deployment.
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.")