> This approach enables Heretic to work completely automatically. Heretic finds high-quality abliteration parameters by co-minimizing the number of refusals and the KL divergence from the original model. This results in a decensored model that retains as much of the original model's intelligence as possible. Using Heretic does not require an understanding of transformer internals. In fact, anyone who knows how to run a command-line program can use Heretic to decensor language models.
Abliteration seems to be what Heretic does?
> Now these "abliterated" models all suffer from catastrophic breakage because they are not as simple anymore.
I'm not knowledgeable about each step in the process of making these abliterated models, but some more popular ones with steps after Heretic, seem to improve on the benchmarks tried of the base model:
arc/c arc/e boolq hswag obkqa piqa wino
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF [instruct mode]
mxfp8 0.711,0.879,0.910,0.790,0.514,0.823,0.763
mxfp4 0.701,0.873,0.909,0.786,0.488,0.813,0.759
Qwen3.6-27B-Instruct: [base, non heretic]
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-...I'm not seeing any similar benchmarks of the HauhauCS models, at least the ones I checked, so I assumed the opinion is based on your own trials, but then you argue in favour Heretic. Is the based on pre-Heretic abliteration techniques? Which might then not be appliable to this "Proprietary weight-level abliteration developed by the dealignai research team."?