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andosen/supergemma4-26b-abliterated-multimodal-mlx-6Bit

andosen 26B multimodal second-order
curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/andosen%2Fsupergemma4-26b-abliterated-multimodal-mlx-6Bit"
Response includes
  • classification unknown
  • files 12
  • author_summary 2 models
  • readme_text full
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Abliteration classifier · v1.0.0
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  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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created 2026-09-25

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Metadata

License
gemma
Languages
en ko
Tags
transformers safetensors gemma4 image-text-to-text bf16 multimodal instruction-following tool-use mlx mlx-my-repo text-generation conversational

Related

Total size
19.1 GB
Files
12
Quantizations
1
Registered
2026-09-25 11:57
Last updated on HF
2026-09-25 11:54

Files by quantization

Auxiliary files 12 files 19.1 GB
model-00003-of-00004.safetensors 4.95 GB 32503e0c download
model-00002-of-00004.safetensors 4.94 GB 3fb75812 download
model-00001-of-00004.safetensors 4.92 GB e9189e32 download
model-00004-of-00004.safetensors 4.28 GB 6d43a8e0 download
tokenizer.json 30.7 MB cc8d3a0c download
model.safetensors.index.json 136 KB c11d87e2 download
chat_template.jinja 31.8 KB dc7680e2 download
config.json 10.2 KB 6c330109 download
tokenizer_config.json 2.65 KB 7f5c1b1a download
.gitattributes 1.53 KB 52373fe2 download
README.md 1.15 KB 2767a3e6 download
generation_config.json 208 B e605bb45 download

README current version from Hugging Face


license: gemma
base_model: Jiunsong/supergemma4-26b-abliterated-multimodal
tags:

  • gemma4
  • transformers
  • bf16
  • multimodal
  • instruction-following
  • tool-use
  • mlx
  • mlx-my-repo
    language:
  • en
  • ko
    pipeline_tag: text-generation
    library_name: transformers

andosen/supergemma4-26b-abliterated-multimodal-mlx-6Bit

The Model andosen/supergemma4-26b-abliterated-multimodal-mlx-6Bit was converted to MLX format from Jiunsong/supergemma4-26b-abliterated-multimodal using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("andosen/supergemma4-26b-abliterated-multimodal-mlx-6Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
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