⚡ Gemma 4 12B Heretic QAT — Q4_0 GGUF

Heretic ARA · QAT-Lossless Q4_0 · 6.4 GB · Encoder-Free Multimodal

Q4_0 12B Dense Heretic Uncensored 6.4 GB QAT Weights Encoder-Free

Uncensored version of Google Gemma 4 12B IT (QAT), processed with Heretic ARA abliteration. Quantized to Q4_0 matching Unsloth's UD-Q4_K_XL format — QAT weights trained for 4-bit quantization, near-lossless quality.

✂️ Heretic ARA Abliteration Parameters

Base: coder3101/heretic-QAT · Heretic v1.2.0 · ARA + Row-Norm

Parameter	Value
start_layer_index	24
end_layer_index	48
preserve_good_behavior_weight	0.3707
steer_bad_behavior_weight	0.0010
overcorrect_relative_weight	0.6177
neighbor_count	15

Metric	Heretic	Original QAT
KL Divergence	0.0575	0 (by definition)
Refusals	8/100	99/100

🏗️ Architecture

Base Model	google/gemma-4-12B-it
Parameters	11.95B (dense, all parameters active)
Architecture	Encoder-free unified multimodal (text + image + audio + video)
Layers	48
Hidden Size	3,840
Attention	16 heads, GQA with 8 KV heads, head dim 256
Context Length	256K tokens (hybrid sliding window 1024 + global attention)
Vocabulary	262K, 140+ languages
Modalities	Text + Image + Audio + Video (encoder-free, native multimodal)
QAT Training	Google official QAT (quantization-aware), weights inherently robust to Q4_0
Quantization	Q4_0 (matching Unsloth UD-Q4_K_XL layout), b9553 llama-quantize

📊 Quantization Details

Format	Q4_0 (uniform — QAT weights optimized for this exact precision)
File Size	6.4 GB
Effective BPW	4.50 (all weight tensors Q4_0, norms F32)
Tool	llama-quantize (b9553, CUDA 13.3)
Source	BF16 GGUF (converted from QAT heretic safetensors)
Context Length	256K (set in GGUF metadata)
QAT Advantage	Q4_0 with QAT weights achieves 88.8% Top-1 vs 74.1% naive Q4_0 (+14.7%)

Why Q4_0? Google's QAT trains weights to be optimal at Q4_0 noise levels. Unsloth's UD-Q4_K_XL uses the same Q4_0 layout — the "dynamic" advantage comes from conversion precision, not per-tensor mixing.

⚙️ Recommended Sampling Parameters

General	temp=1.0, top_p=0.95, top_k=64
Coding	temp=0.6, top_p=0.95, top_k=64

Use --jinja flag with llama.cpp. Disable thinking: --chat-template-kwargs '{"enable_thinking":false}'.

📝 Usage

Compatible with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF runtimes. Fits easily on 8GB VRAM. Encoder-free — no separate vision/audio projector needed.

llama-server \
 -m gemma-4-12B-it-heretic-QAT-UD-Q4_K_XL.gguf \
 --jinja -ngl 99 -c 8192 \
 --port 8001

🔗 Credits

Heretic Abliteration: coder3101 · Heretic v1.2.0 ARA + Row-Norm
QAT Weights: Google Gemma 4 12B IT
Quantization Recipe: Unsloth UD-Q4_K_XL (Q4_0 layout)
Quantization Tool: llama.cpp b9553 · GitHub
Original Model: Google Gemma 4 12B IT