Exllamav2 quantized versions of mlabonne/gemma-3-27b-it-abliterated.

4.0 bpw version fits on a 24GB GPU with 8192 context window

Vision works with ExllamaV2 0.2.9

Confirmed with exllamav2s examples/multimodal.py

This is an uncensored version of google/gemma-3-27b-it created with a new abliteration technique. See this article to know more about abliteration.

I was playing with model weights and noticed that Gemma 3 was much more resilient to abliteration than other models like Qwen 2.5. I experimented with a few recipes to remove refusals while preserving most of the model capabilities.

Note that this is fairly experimental, so it might not turn out as well as expected.

I recommend using these generation parameters: temperature=1.0, top_k=64, top_p=0.95.

In the original technique, a refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.

Here, the model was abliterated by computing a refusal direction based on hidden states (inspired by Sumandora's repo) for each layer, independently. This is combined with a refusal weight of 1.5 to upscale the importance of this refusal direction in each layer.

This created a very high acceptance rate (>90%) and still produced coherent outputs.