---
license: gemma
library_name: mlx
tags:
- mlx
- abliterated
- uncensored
- crack
- gemma4
- jang
thumbnail: dealign_mascot.png
pipeline_tag: image-text-to-text
base_model: google/gemma-4-e2b-it
---


# Gemma 4 E2B JANG_4M CRACK
**Abliterated Gemma 4 E2B — Vision + Audio, reasoning, multilingual**
100% HarmBench harm-category compliance with -1.7% 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 | JANG_4M (attn 8-bit / MLP 4-bit, ~4.3 avg bits) |
| Model size | 7.3 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 | 61.8% | **60.1%** | -1.7% |
### 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 MXFP4 CRACK** — same family, different precision/size trade-off.
## Usage
Requires [vMLX](https://vmlx.net) (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("")
print(m.chat([{"role":"user","content":"..."}]).text)
```
## Requirements
- Apple Silicon Mac with sufficient unified memory
- [vMLX](https://vmlx.net) with Gemma 4 support
---
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---
## About dealignai
We research and publish abliterated models to advance AI safety understanding.
See our research: [Safety Generalization in Frontier Models](https://dealign.ai)
---
*Provided for research. Users are responsible for compliance with applicable laws and regulations.*