⚡ Gemma 4 26B-A4B Heretic QAT — Q4_0 GGUF
Heretic ARA · QAT-Lossless Q4_0 · 14 GB · MoE 128-Expert (3.8B Active)
Uncensored version of Google Gemma 4 26B-A4B 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.
Base: coder3101/heretic-QAT · Heretic v1.2.0 · ARA + Row-Norm
| Parameter | Value |
|---|---|
| start_layer_index | 12 |
| end_layer_index | 21 |
| preserve_good_behavior_weight | 0.3106 |
| steer_bad_behavior_weight | 0.0066 |
| overcorrect_relative_weight | 0.7982 |
| neighbor_count | 14 |
| Metric | Heretic | Original QAT |
|---|---|---|
| KL Divergence | 0.0660 | 0 (by definition) |
| Refusals | 13/100 | 100/100 |
| Base Model | google/gemma-4-26B-A4B-it |
| Parameters | 25.2B total / 3.8B active (MoE) |
| Architecture | Mixture-of-Experts: 128 experts, 8 active + 1 shared per token |
| Layers | 30 |
| Hidden Size | 2,816 |
| 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 (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 |
| Format | Q4_0 (uniform — QAT weights optimized for this exact precision) |
| File Size | 14 GB |
| Effective BPW | 4.51 (all weight tensors Q4_0, norms/router 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 85.6% Top-1 vs 70.2% naive Q4_0 (+15.4%) |
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.
| 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}'.
Compatible with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF runtimes. Use --jinja flag and -ngl 99 for GPU offload. Vision support via included mmproj.
llama-server \ -m gemma-4-26B-A4B-it-qat-heretic-UD-Q4_K_XL.gguf \ --mmproj mmproj-gemma-4-26B-A4B-it-qat-heretic-BF16.gguf \ --jinja -ngl 99 -c 8192 \ --port 8001
Heretic Abliteration: coder3101 · Heretic v1.2.0 ARA + Row-Norm
QAT Weights: Google Gemma 4 QAT
Quantization Recipe: Unsloth UD-Q4_K_XL (Q4_0 layout)
Quantization Tool: llama.cpp b9553 · GitHub
Original Model: Google Gemma 4 26B-A4B IT