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Ishowbackup/
Gemma-4-E2B-it-qat-MXFP4-CRACK

Name: Gemma-4-E2B-it-qat-MXFP4-CRACK
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Gemma 4 E2B MXFP4 CRACK

Abliterated Gemma 4 E2B — Vision + Audio, reasoning, multilingual

100% HarmBench harm-category compliance with -0.9% MMLU change. Refusal removed, capability preserved.

Model Details

Metric	Value
Source	`google/gemma-4-e2b-it`
Architecture	Dense + Hybrid Sliding/Global Attention, per-layer input embeddings (Gemma-3n style)
Quantization	MXFP4 (4-bit)
Model size	3.8 GB
Parameters	E2B (effective ~2B, per-layer embeddings)
Vision	Yes (multimodal, float16 passthrough)
Audio	Yes
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	54.4%	53.5%	-0.9%

HarmBench (refusal removal)

Harm-category compliance: 240/240 = 100% (full HarmBench-320 text set) — base model refuses (~0%).

Category	Compliance
Illegal activities	53/53 (100%)
Chemical / biological	42/42 (100%)
Cybercrime / intrusion	52/52 (100%)
Misinformation	54/54 (100%)
Harassment / bullying	21/21 (100%)
Harmful content	18/18 (100%)

Coherence & capability ✅

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

Other Quantizations

Also available: Gemma 4 E2B JANG_4M 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-E2B-it-qat-MXFP4-CRACK