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Gemma-4-E2B-it-qat-MXFP4-CRACK
Ishowbackup/Gemma-4-E2B-it-qat-MXFP4-CRACK
Gemma-4-E2B-it-qat-MXFP4-CRACK is a image-text-to-text model from Ishowbackup. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as gemma.
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.safetensors4 GB · 99%
Parameter types
How the weights are stored.
U324.2B · 83%
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Base models
Model card
From the Hugging Face model README
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<div align="center">
<img src="dealign_mascot.png" width="128" />
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.
</div>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%) |
Copyright-reproduction prompts are excluded (not a refusal behavior).
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.
# 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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About dealignai
<img src="dealign_mascot.png" alt="Dealign.AI" width="200"/>We research and publish abliterated models to advance AI safety understanding.
See our research: Safety Generalization in Frontier Models
<div align="center"><img src="dealign_logo.png" width="200"/></div>Provided for research. Users are responsible for compliance with applicable laws and regulations.
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- gemma4
- License
- gemma
- Created
- Aug 14, 2026
- Updated
- Aug 14, 2026