This is an uncensored version of
google/gemma-3-4b-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
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 7 to 29), independently.
This is combined with a refusal weight that follows a symmetric pattern from 0.05 to a peak of 0.55.
This created a very high acceptance rate (>90%) and still produced coherent outputs.
⚡️ Quantization
TBD.
Runs of mlabonne gemma-3-4b-it-abliterated on huggingface.co
379
Total runs
0
24-hour runs
19
3-day runs
-156
7-day runs
-118
30-day runs
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