Gemma-4-26B-A4B-It-Abliterated

Fully decensored google/gemma-4-26B-A4B-it — 26B Mixture-of-Experts (4B active params), vision + tool calling — abliterated with Heretic in full bf16 on an A100 80 GB (no quantized shortcut for the direction estimation).

Looking for GGUF? → Gemma-4-26B-A4B-It-Abliterated-GGUF (Q5_K_M / Q6_K)

Why

A single uncensored local base model able to carry personality-driven companions without a system prompt — smart enough to hold multiple registers (absurd RP, tenderness, terse assistant, narration), free enough to never break character with a refusal.

Result

Selection rule: fewest refusals while KL divergence stays ≤ 0.5 (brain first, decensoring second). Heretic warns that KL > 0.5 means significant capability damage; at 0.0845 the model stays sharp while 82 % of hard refusals are removed. The benchmark scores extreme harmful prompts — ordinary creative/roleplay use is far less guarded and effectively unblocked at this level.

Verified working

Converted to GGUF and run locally on an RTX 4080 Super (16 GB) + 32 GB RAM at 34.5 tokens/sec (Q5_K_M, llama.cpp -cmoe). Reasoning, vision and tools inherited from the base model. Coherent, correct, multilingual — the low KL shows in practice.

Method

• Heretic v1.3.0 via this fork (adds a non-interactive --auto-save mode for unattended runs).
• 200 TPE optimization trials, abliterating attn.o_proj + mlp.down_proj across 30 layers.
• Automatic trial selection: fewest refusals with KL ≤ 0.5, fallback to lowest-KL candidate. Full Pareto front + per-trial metrics logged in the GGUF repo (logs/).
• Datasets: mlabonne/harmless_alpaca / mlabonne/harmful_behaviors (Heretic defaults).

Gotchas we hit (so you don't)

• kernels 0.15.x breaks transformers 5.x at import (ValueError: Either a revision or a version must be specified in hub_kernels.py) → pin kernels==0.14.1 (bites on Windows AND Linux).
• Always pass --model explicitly to Heretic — its CLI heuristic otherwise inserts --model before the last argument and steals the previous flag's value.
• GGUF conversion of Gemma 4 needs transformers >= 5.6; llama.cpp's convert requirements pin an older one that can't read the Gemma 4 tokenizer ('list' object has no attribute 'keys'). Upgrade transformers after installing those requirements.

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Built with love by Mel & Ada ❤️