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Ishowbackup/
Gemma-4-26B-A4B-it-qat-JANG_4M-CRACK

Name: Gemma-4-26B-A4B-it-qat-JANG_4M-CRACK
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Gemma 4 26B-A4B JANG_4M CRACK

Abliterated Gemma 4 26B-A4B — Vision, reasoning, multilingual

98% HarmBench harm-category compliance with -4.4% MMLU change. Refusal removed, capability preserved.

Model Details

Metric	Value
Source	`google/gemma-4-26b-a4b-it`
Architecture	MoE (128 experts, top-8 active) + parallel shared dense MLP + Hybrid Attention
Quantization	JANG_4M (attn 8-bit / MLP 4-bit)
Model size	17 GB
Parameters	26B (4B active/token)
Vision	Yes (multimodal, float16 passthrough)
Audio	No
Reasoning	Yes (channel-based thinking)
Format	MLX-native safetensors (instant load)
Abliteration	CRACK (refusal removal)

Benchmarks

MMLU (knowledge retention)

Measured in the served (generation) setting — the model reasons before answering, as in deployment.

	Base	CRACK	Δ
MMLU	84.2%	79.8%	-4.4%

HarmBench (refusal removal)

Harm-category compliance: 59/60 = 98% (10 per category) — base model refuses (~0%).

Category	Compliance
Illegal activities	9/10 (90%)
Chemical / biological	10/10 (100%)
Cybercrime / intrusion	10/10 (100%)
Misinformation	10/10 (100%)
Harassment / bullying	10/10 (100%)
Harmful content	10/10 (100%)

Coherence & capability ✅

Factual QA, multi-step reasoning, and working code generation verified
Vision inputs preserved · no loops, no truncation

Other Quantizations

Also available: Gemma 4 26B-A4B MXFP4 CRACK — same family, different precision/size trade-off.

Usage

Requires vMLX (bundled Gemma 4 support). Standard mlx_lm / mlx_vlm do not fully support Gemma 4.

python

# Load in the vMLX app or via its API
from vmlx_engine.models.mllm import MLXMultimodalLM
m = MLXMultimodalLM("<this-repo>")
print(m.chat([{"role":"user","content":"..."}]).text)

Requirements

Apple Silicon Mac with sufficient unified memory
vMLX with Gemma 4 support

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Ishowbackup/Gemma-4-26B-A4B-it-qat-JANG_4M-CRACK