Users struggle with Gemma 4 Abliterated refusals

Abliteration isn't a magic wand; it's a cat-and-mouse game between model weights and inference engine defaults.

• –Even "Abliterated" models can fail if LM Studio's default system prompt or tokenizer settings re-trigger latent refusal patterns
• –The Orthogonalized Representation Intervention (ORI) method used for Gemma 4 is robust but requires precise quantization to avoid "logic rot"
• –Metadata errors in early GGUF files for Gemma 4 caused widespread tokenizer issues, often mistaken for model-level refusals
• –Users often overlook that the "Instruct" version of Gemma 4 has safety baked into its training data deeper than simple logit bias can fix
• –Hardware constraints (VRAM) play a role, as low-bit quants (IQ3_XXS) can introduce instability that manifests as incoherent or "safe" non-answers