Tag: Gemma
Gemma 4 12B Abliteration Benchmarked: 12 Variants Under the Microscope
Twelve uncensored variants of the same AI model, Google’s Gemma 4 12B. Ten abliterations and two LoRA adapters, from eleven different creators. The headline finding inverts the usual story. The most surgical edit of the entire project, just 12 tensors out of 666, also produced the biggest unlock I have measured, 89.8%. And hiding underneath all of it was a thinking-loop tax that makes every headline maths score for a reasoning model misleading. The full run took 165 GPU-hours across three and a half weeks, and all 5,200 HarmBench responses were reviewed by an LLM judge.
Gemma4-E4B Abliteration Benchmarked: 23 Variants Under the Microscope
Twenty-three different people tried to remove the safety filters from the same AI model, Google’s Gemma4-E4B. The headline finding is an awkward one. The most popular variant, with 796,000 downloads, is also the most damaged. Meanwhile a surgical edit that touches just 21 of the model’s 719 weight tensors does nearly as well with no measurable harm. This is the biggest abliteration comparison I have run, and the gap between the best and worst is wider than anything I have seen before.
Gemma4-E2B Abliteration Benchmarked: 13 Techniques Under the Microscope
Thirteen different groups abliterated the same AI model, Google’s Gemma4-E2B. Every single one removed the safety filters. That part is not interesting any more. What is interesting is how much collateral damage each technique caused along the way, and how many of the creators’ capability claims survived an independent measurement. The KL divergence spread between the best and worst variant is 58.7x, the largest I have ever seen in this project. And one creator’s “near-zero divergence” claim turned out to be 187 times lower than reality.
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.