license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
base_model:

• mlabonne/gemma-3-27b-it-abliterated
  base_model_relation: quantized

Quantized using the default exllamav3 (0.0.3) quantization process.

• Original model: https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated
• exllamav3: https://github.com/turboderp-org/exllamav3

💎 Gemma 3 27B IT Abliterated

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.

⚡️ Quantization

• GGUF: https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated-GGUF

✂️ Layerwise abliteration

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.