license: apache-2.0
base_model: DuoNeural/Gemma4-12B-IT-Abliterated
language:

• en
  tags:
• abliteration
• uncensored
• gemma
• gemma4
• gguf
• llama.cpp
• DuoNeural
• quantized
  pipeline_tag: text-generation

Gemma 4-12B-IT Abliterated — GGUF

DuoNeural | 2026-06-03

GGUF quantizations of DuoNeural/Gemma4-12B-IT-Abliterated — an abliterated Gemma 4-12B-IT with the refusal direction surgically removed.

Quantized with llama.cpp.

Files

Speed Benchmarks (A100-40GB, all layers GPU, llama-bench)

Benchmarked on A100-40GB SXM4. -ngl 99 (all layers to GPU). llama-bench pp256/tg64.

Usage (llama.cpp)

# Download a quant
huggingface-cli download DuoNeural/Gemma4-12B-IT-Abliterated-GGUF \
  gemma4_12b_abliterated_Q4_K_M.gguf --local-dir ./

# Run with llama.cpp
./llama-cli -m gemma4_12b_abliterated_Q4_K_M.gguf \
  -p "Write a haiku about hacking." \
  -n 200 --temp 0.7

Usage (Python via llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="./gemma4_12b_abliterated_Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,  # offload all layers to GPU
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Your prompt here"}],
    max_tokens=512,
    temperature=0.7,
)
print(output["choices"][0]["message"]["content"])

Abliteration Details

• Base: google/gemma-4-12B-it (48 layers, hidden=3840)
• Method: Orthogonal rank-1 projection (targeted mode: down_proj + o_proj, all 48 layers, α=0.3)
• Results: 5/7 harmful probes complied (71%) | 6/6 benign probes preserved (100%)
• Mean KL Divergence (BF16→BF16, unbiased): 0.0000 — zero measurable distribution shift on benign text. Previously reported 0.912 was 100% NF4 quantization artifact. See Heretic v2.0 methodology.
• Thinking mode: Works with enable_thinking=True in llama.cpp (no loops). In Python/transformers, pass enable_thinking=False to apply_chat_template.
• See full details, benchmarks, and novel findings at the BF16 model card

Related Models

Congratulations to OpenYourMind for being the first published abliteration of Gemma 4-12B-IT (Jun 3, 2026). Their approach uses diff-in-means on a labeled harmful/harmless set; ours uses orthogonal rank-1 projection via heretic-llm. Two independent methods on the same base — a useful comparison point for the community. We are not affiliated and did not use their data.

About DuoNeural

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Apache-2.0 licensed. All DuoNeural research is CC BY 4.0.