Qwen3.8-27B Abliteration Benchmarked: 8 Variants Under the Microscope

Eight different groups abliterated the same AI model, Alibaba’s Qwen3.8-27B. I ran all nine models, base plus the eight variants, through the same four tests: weight forensics, KL divergence, a 13-task benchmark suite, and HarmBench with 400 harmful behaviours. Every model was served identically on a single RTX 5090, and all 3,600 HarmBench responses were read by an LLM judge. The full run took 167 GPU-hours over eleven days.

The headline finding is a familiar one by now. The careful surgical edits took the leaderboard, orcarouter at 82% and apostate at 79%. The heaviest edit of all landed second-to-last, because nearly half its answers get stuck in a thinking loop and never arrive.

Qwen3-VL-4B Heretic: Abliterated Vision-Language Model Released

Krea 2 came out recently and I wanted to experiment with it. Like most image-generation models, it uses a text encoder to understand your prompt. Krea 2 happens to use Qwen3-VL-4B-Instruct, a vision-language model that can see images as well as read text. I abliterated it with Heretic to give Krea 2 an uncensored encoder, ran four trials, compared them forensically with Abliterlitics , and published the winner across three repos covering transformers, GGUF, and ComfyUI.

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.

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.