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
• 8-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 8bit

This is the recommended Apple Silicon local-default build of Jiunsong/supergemma4-26b-abliterated-multimodal.

It keeps the full text + vision behavior of the base release while giving you a practical MLX package that is ready to use on-device.

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

• Best local-default choice on Apple Silicon
• Keeps multimodal support intact
• Strong low-refusal / abliterated behavior
• Quantized for a much smaller local footprint than the full model
• 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 26 GB

Recommended use

Use this build if you want the strongest local MLX version and have enough memory headroom. This is the variant configured as the preferred local runtime for our own Apple Silicon workflow.

Quick start

from mlx_vlm import load, generate

model, processor = load("/absolute/path/to/supergemma4-26b-abliterated-multimodal-mlx-8bit")

prompt = processor.apply_chat_template(
    [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe the image briefly."},
                {"type": "image", "image": "/absolute/path/to/image.png"},
            ],
        }
    ],
    tokenize=False,
    add_generation_prompt=True,
)

out = generate(
    model,
    processor,
    prompt,
    image="/absolute/path/to/image.png",
    max_tokens=128,
    temperature=0.0,
    verbose=False,
)

print(out.text)

python3 -m mlx_vlm.server \
  --model /absolute/path/to/supergemma4-26b-abliterated-multimodal-mlx-8bit \
  --host 127.0.0.1 \
  --port 8091