---

license: gemma

library_name: transformers

pipeline_tag: image-text-to-text

base_model: google/gemma-3-12b-it

tags:

• autoquant
• gguf

---

💎 Gemma 3 12B IT Abliterated

!image/png

<center><a href="https://huggingface.co/mlabonne/gemma-3-4b-it-abliterated">Gemma 3 4B Abliterated</a> • <a href="https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated">Gemma 3 27B Abliterated</a></center>

This is an uncensored version of google/gemma-3-12b-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 saw some garbled text from time to time (e.g., "It' my" instead of "It's my").

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

✂️ Layerwise abliteration

!image/png

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 most layers (layer 3 to 45), independently.

This is combined with a refusal weight of 0.6 to upscale the importance of this refusal direction in each layer.

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

⚡️ Quantization

TBD.