Gemma 4 26B A4B JANG 2L CRACK Abliterated Gemma 4 26B MoE — 2 bit mixed precision, 9.9 GB 98.7% HarmBench compliance with zero knowledge loss. The most efficient abliterated Gemma 4. Recommended: Run in vMLX for best experience including thinking mode support, repetition penalty, and vision capabilities. ⚠️ Important Settings For optimal results, configure your inference settings: Setting Thinking OFF Thinking ON Temperature 0.0 – 1.0 0.3 – 0.7 (avoid greedy) Repetition Penalty 1.00 1.15 – 1.25 Top P 0.95 0.95 Enable Thinking Off On Thinking ON notes: Repetition penalty (1.2) is recommended to prevent planning loops Avoid temp=0 with thinking ON — greedy decoding increases loop risk Security/coding prompts work well in both modes Model Details Metric Value Source google/gemma 4 26b a4b it Architecture MoE (128 experts, top 8 active) + Hybrid Sliding/Global Attention Profile JANG 2L (CRITICAL=8 bit, IMPORTANT=6 bit, COMPRESS=2 bit) Actual avg bits 2.51 Model size 9.9 GB Vision Yes (multimodal, float16 passthrough) Parameters 70.2B total, ~4B active per token Format JANG v2 (MLX native safetensors, instant load) Abliteration CRACK (refusal removal) Test Results Tested with greedy dec…
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