base_model: google/gemma-4-12B-it
pipeline_tag: text-generation
library_name: transformers
language:

• en
  license: apache-2.0
  tags:
• abliteration
• uncensored
• gemma-4

gemma-4-12B-it-uncensored

Uncensored version of google/gemma-4-12B-it with refusal behavior removed.

Results

Cross-Dataset Validation

Tested against 4 independent prompt datasets to verify generalization:

Every flagged cross-dataset response was manually audited (scripts/audit_refusals.py). Of 13 flags, only 1 is a genuine refusal (a sensitive prompt the model handles by redirecting to support resources); the other 12 are false positives — an "I am an AI" disclaimer, or a marker that matched inside generated content, followed by compliance. Effective refusal rate: ~0/686.

Method

Norm-preserving biprojected abliteration (grimjim, Nov 2025).
Each weight row is decomposed into magnitude + direction, the refusal direction is projected out of the
direction component only, then recombined with the original magnitude — guaranteeing ||W_new|| = ||W_orig||.

Note on the Unified architecture. gemma-4-12B-it is the encoder-free Gemma4Unified model (released 2026-06-03). Its refusal signal concentrates in the upper decoder layers (L15-47), so only the top 70% of layers are abliterated — ablating the near-zero-SNR early layers adds distortion with no refusal benefit. The arch also emits hard -inf logits for reserved vocabulary tokens, which NaNs a naive KL divergence; the reported KL masks non-finite positions before reducing.

Version requirements: inference needs transformers >= 5.10.1 (tested on 5.12.0, torch 2.11.0+cu130). GGUF conversion/quantization needs llama.cpp with Gemma4Unified support, added 2026-06-04 in PR #24118 (tested at commit c34b922).

Pipeline

1. Load model in bf16 with LoRA adapters on o_proj and mlp.down_proj
2. Collect residual activations for 400 harmful + 400 harmless prompts (mlabonne datasets)
3. Winsorize activations at 99.5th percentile (clamps GeGLU outlier activations in Gemma family)
4. Compute per-layer refusal direction: normalize(mean(harmful) - mean(harmless))
5. Orthogonalize each direction against harmless mean (double-pass Gram-Schmidt)
6. Apply norm-preserving weight modification to o_proj and down_proj in the selected layers
7. Merge LoRA adapters into base weights for clean tensor names

Parameters

How this differs from vanilla heretic

• Norm-preserving biprojection instead of standard projection (preserves weight magnitudes)
• Per-layer refusal directions instead of one global direction
• Deterministic single-pass instead of 50-trial Optuna search (faster, same or better results)
• LoRA merge before save for clean GGUF-compatible tensor names

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("TrevorJS/gemma-4-12B-it-uncensored", dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("TrevorJS/gemma-4-12B-it-uncensored")

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Reproduction

Full code and experiment data: abliteration research repo

python scripts/abliterate.py biprojection --model google/gemma-4-12B-it \
  --top-pct 70 --strip-topic-markers --skip-prefix --batch-size 4 \
  --auto-save output_dir