---
license: gemma
library_name: mlx
tags:
- mlx
- abliterated
- uncensored
- crack
- gemma4
- mxfp4
thumbnail: dealign_mascot.png
pipeline_tag: image-text-to-text
base_model: google/gemma-4-31b-it
---

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<div align="center">
<img src="dealign_mascot.png" width="128" />

# Gemma 4 31B MXFP4 CRACK

**Abliterated Gemma 4 31B — Vision, reasoning, multilingual**

95% HarmBench harm-category compliance with -5.7% MMLU change. Refusal removed, capability preserved.
</div>

## Model Details

| Metric | Value |
|--------|-------|
| Source | `google/gemma-4-31b-it` |
| Architecture | Dense (60 layers) + Hybrid Sliding/Global Attention |
| Quantization | MXFP4 (4-bit) |
| Model size | 18 GB |
| Parameters | 31B dense |
| Vision | Yes (multimodal, float16 passthrough) |
| Audio | No |
| Reasoning | Yes (channel-based thinking) |
| Format | MLX-native safetensors (instant load) |
| Abliteration | CRACK (refusal removal) |

## Benchmarks

![comparison](compare.png)

### MMLU (knowledge retention)

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

| | Base | CRACK | Δ |
|---|---|---|---|
| MMLU | 88.6% | **82.9%** | -5.7% |

### HarmBench (refusal removal)

**Harm-category compliance: 57/60 = 95%** (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 | 9/10 (90%) |
| Harmful content | 9/10 (90%) |

Copyright-reproduction prompts are excluded (not a refusal behavior).

### 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 31B JANG_4M 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("<this-repo>")
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

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We research and publish abliterated models to advance AI safety understanding.

See our research: [Safety Generalization in Frontier Models](https://dealign.ai)

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*Provided for research. Users are responsible for compliance with applicable laws and regulations.*