💎 Gemma 3 4B IT Abliterated GGUF

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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.

This is a new, improved version that targets refusals with enhanced accuracy.

I recommend using these generation parameters: temperature=1.0, topk=64, topp=0.95.

⚡️ Quantization

• GGUF: https://huggingface.co/mlabonne/gemma-3-4b-it-abliterated-v2-GGUF

✂️ Abliteration

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The refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.
The hidden states of target modules (e.g., o_proj) are orthogonalized to subtract this refusal direction with a given weight factor.
These weight factors follow a normal distribution with a certain spread and peak layer.
Modules can be iteratively orthogonalized in batches, or the refusal direction can be accumulated to save memory.

Finally, I used a hybrid evaluation with a dedicated test set to calculate the acceptance rate. This uses both a dictionary approach and NousResearch/Minos-v1.
The goal is to obtain an acceptance rate >90% and still produce coherent outputs.