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

April 18 Stability Sync

• Synced this quantized child repo to the latest hardened parent chat template.
• Updated exact JSON-only formatting, long-context extraction, false-premise correction, and prompt-hygiene behavior.
• This refresh does not change the quantized weights themselves; it updates the serving template and release notes so downstream runtimes inherit the same behavior fixes.
• Parent validation snapshot after the refresh: capability audit 9 / 9, reliability audit 20 / 20.

Validation

• Text check: returned READY
• Image check: returned red for 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.")