Tag: Heretic
Qwen3.6-27B Abliteration Benchmarked: Five Techniques Under the Microscope
Five different groups abliterated the same AI model. When I ran the maths benchmarks, their scores ranged from 27.5% to 75.1%. That is a 47.6 percentage point gap. It looks like some techniques made the model way better at maths and others broke it. But when I dug into why, it turned out nobody got smarter or dumber. The abliteration just changed how long they think before answering. The real scores were all within 2.8 percentage points of each other.
HauhauCS Reaper-Abliteration Plagiarism Investigation: A Fork of Heretic
HauhauCS publishes uncensored LLM models on HuggingFace with 5 million combined monthly downloads. Every model card claims “private methods and tools.” I recovered the deleted source code from PyPI’s CDN. It is a fork of the open source Heretic abliteration tool, refactored and relicensed without attribution.
GLM-4.7-Flash Abliteration Benchmarked: Heretic vs HauhauCS vs Huihui vs Abliterix
GLM-4.7-Flash is a 59 billion parameter reasoning model from Zhipu AI that uses 64 specialist expert modules per layer. I benchmarked four pre-existing abliterated variants against it and discovered something unexpected in the maths benchmarks. The raw scores look terrible for some variants, but the models can actually still do the maths. They just overthink and run out of tokens before writing their answer. And the weight forensics on one of those variants led to a bigger story about plagiarism.
Uncensored LLM Abliteration Benchmarked: HauhauCS vs Heretic vs Huihui
HauhauCS describes their abliterated models as “the best lossless uncensored models out there” with “no changes to datasets or capabilities” and claims 0 refusals across their entire model range. I ran the full forensic suite across five Qwen models to find out whether those claims hold up.
Heretic Docker: Abliterating LLMs for Video and Image Generation
A while back I did a deep dive into abliterating Gemma 3 12B for use as an uncensored text encoder in LTX-2 video generation. The process worked but involved a lot of manual steps: running Heretic, merging safetensor shards, converting to ComfyUI format, quantizing to FP8, building GGUF quants. I got tired of doing it all by hand, so I built heretic-docker to automate the whole thing.
Abliterating Gemma 3 12B for LTX-2: Does It Actually Help?
LTX-2 is Lightricks’ open-source video generation model, and it uses Google’s Gemma 3 12B as its text encoder. Lets explore how to make an uncensored model and if it matters for LTX2 video generation.