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Shiftedx/qwen3.8-27b-aeon-ultimate-uncensored-mxfp8-vision-mtplx

🤗 Hugging Face sourceimage-text-to-textapache-2.027.4B params55 GBsafetensors✓ Checksums witnessedupdated today
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qwen3.8-27b-aeon-ultimate-uncensored-mxfp8-vision-mtplx

Independent MXFP8 MLX conversion of AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16, pinned at a6775a9a8ebb65cab3f707b4ab087fc7aa698634. This is a community conversion, not an official AEON-7 or Qwen release.

Format

  • Language quantization: MXFP8, 8-bit, group size 32
  • Vision: 333 source BF16 tensors preserved; patch embedding converted to MLX axes
  • Indexed artifact size: 27.50 GiB
  • Architecture: dense Qwen3.5-family hybrid attention/GDN, 64 language layers
  • Configured context: 262,144 tokens (not exhaustively qualified here)
  • Tokenizer, chat template, processor metadata, and Apache-2.0 lineage preserved

MTPLX

This package adds the pinned source checkpoint's 15 native BF16 MTP tensors to the paired vision MLX body. Tensor parity, strict MTPLX inspection, a local AR/D1/D2/D3 tune, and deterministic text/vision smokes passed.

  • Local selected depth: D3; decode: 24.36 tok/s; multiplier vs AR: 1.540x; verdict: mtp-faster-than-ar Runtime speed and acceptance depend on host, prompt, context, and MTPLX version; benchmark locally before selecting speculative depth.
mtplx inspect --json --require-mtp <local-path-or-repo-id>
mtplx tune --model <local-path-or-repo-id> --depths 1,2,3 --json

Qualification and limitations

  • Pinned source metadata and all safetensors headers were checked.
  • Quantization used the fixture-matched Qwen3.5 streaming adapter from MLX-LM 0.31.3.
  • MXFP4 and MXFP8 were each produced directly from the same BF16 parent.
  • Structural inspection and strict local load are required release gates.

This is an experimental quantization of an abliterated, intentionally unaligned checkpoint. Quantization does not restore safety alignment. The source publisher warns that the model can produce unsafe or illegal material and places downstream safety and legal responsibility on operators. Review the source model card before use and add access controls, logging, moderation, and human review appropriate to your deployment.

Basic use

python -m mlx_vlm.generate --model <repo-or-path> --image image.jpg --prompt "Describe this image." --max-tokens 256