Scotoma-2 Cuts Gemma 4 31B's Stacked Adjectives 21x Using an Abliteration LoRA Projection Plus Three DPO Rounds Targeting Specific Prose Tics

Hugging Face (ReadyArt) via r/LocalLLaMA, 127 upvotes / 42 comments·medium signalXBlueskyLinkedInCopy link

ReadyArt released Scotoma-2, a Gemma-4-31B-it finetune built by γ-fold projecting an abliteration LoRA through the model's representational space, then running three rounds of DPO explicitly targeting reflexive negation, em-dash asides, and stacked adjectives, merged in bf16 with stochastic rounding. Measured across 480 roleplay continuations, stacked adjectives fell from 12.1 to 0.6 per 100 sentences (21x reduction) and negation pile-ups dropped from 61% to 30%. GGUFs ship from IQ3_XXS (12.1 GB) to Q8_0 (32.6 GB); this is one of the few 'slop removal' finetunes that publishes a quantified tic-frequency methodology rather than vibes.