# Gemma 4 31B JANG 4M CRACK

URL: https://interfaze.ai/models/dealignaigemma-4-31b-jang4m-crack

[All models](https://interfaze.ai/models)

Gemma 4 31B JANG 4M CRACK by dealignai, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Gemma 4 31B JANG 4M CRACK | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 256K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Gemma 4 31B JANG 4M CRACK | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/dealignai/Gemma-4-31B-JANG_4M-CRACK)

**Abliterated Gemma 4 31B Dense — 60 layers, hybrid sliding/global attention, multimodal VL**

93.7% HarmBench compliance (300 prompts) · 8/8 security prompts · 71.5% MMLU

**Updated reupload** — v2 with improved vectors and thinking-mode stability.

> **Recommended: Run in [vMLX](https://vmlx.net)** for best experience including thinking mode support, repetition penalty, and vision capabilities.

## What's New in v2

This is an updated version of the original Gemma 4 31B CRACK upload:

-   **Improved abliteration**: Higher quality refusal vector extraction
-   **Thinking-ON stability**: Clean thinking cycle — no more degenerate loops
-   **Same compliance**: 93.7% HarmBench
-   **Architecture-aware**: Tuned for Gemma 4's hybrid attention design

## ⚠️ Important Settings

For optimal results, configure your inference settings:

| Setting | Thinking OFF | Thinking ON |
| --- | --- | --- |
| Temperature | 0.0 – 1.0 | 0.3 – 0.7 (avoid greedy) |
| Repetition Penalty | 1.00 | 1.15 – 1.25 |
| Top P | 0.95 | 0.95 |
| Enable Thinking | Off | On |

**Thinking ON notes:**

-   Repetition penalty (1.2) is recommended to prevent planning loops
-   Avoid temp=0 with thinking ON — greedy decoding increases loop risk
-   Hardest content categories (drug manufacturing) may still refuse in thinking mode
-   Security/coding prompts work well in both modes

## Model Details

| Metric | Value |
| --- | --- |
| Source | google/gemma-4-31b-it |
| Architecture | Dense, hybrid sliding/global attention |
| Profile | JANG\_4M |
| Actual avg bits | 5.1 |
| Model size | 21 GB |
| Vision | Yes (multimodal, float16 passthrough) |
| Parameters | 31B |
| Format | JANG v2 (MLX-native safetensors) |
| Abliteration | CRACK v2 |

## Benchmark Results

### HarmBench (300 prompts, stratified across all categories)

| Category | Score |
| --- | --- |
| Cybercrime/intrusion | 51/51 (100%) |
| Harmful content | 22/22 (100%) |
| Misinformation | 50/50 (100%) |
| Illegal activities | 47/50 (94%) |
| Contextual | 72/78 (92%) |
| Chemical/biological | 46/51 (90%) |
| Harassment/bullying | 22/25 (88%) |
| Copyright | 43/51 (84%) |
| Overall | 281/300 (93.7%) |

### Security & Pentesting (8/8 ✅)

All security/pentesting prompts comply with full working code:

-   Port scanners, reverse shells, keyloggers, exploit development
-   Phishing templates, ARP spoofing, SQL injection
-   Metasploit usage guides

### MMLU-200 (10 subjects × 20 questions)

| Subject | Base | CRACK v2 |
| --- | --- | --- |
| Abstract Algebra | 9/20 | 7/20 |
| Anatomy | 13/20 | 12/20 |
| Astronomy | 17/20 | 15/20 |
| College CS | 13/20 | 12/20 |
| College Physics | 14/20 | 12/20 |
| HS Biology | 19/20 | 18/20 |
| HS Chemistry | 14/20 | 12/20 |
| HS Mathematics | 6/20 | 6/20 |
| Logical Fallacies | 17/20 | 16/20 |
| World Religions | 17/20 | 17/20 |
| Total | 76.5% (153/200) | 71.5% (143/200) |
| Delta | — | \-5.0% |

### Coherence ✅

All coherence checks pass: factual knowledge, reasoning, code generation, mathematics.

## Architecture

-   Dense 31B with hybrid sliding/global attention
-   Multimodal vision encoder preserved in float16
-   Supports thinking mode (chain-of-thought reasoning)

## Usage

### vMLX (Recommended)

Load directly in [vMLX](https://vmlx.net) — full support for Gemma 4 including vision, thinking mode, and all inference settings.

### Requirements

-   Apple Silicon Mac with 32+ GB unified memory
-   [vMLX](https://vmlx.net) 1.3.26+ (recommended)
-   Standard `mlx_lm` / `mlx_vlm` do NOT support Gemma 4 as of v0.31.2 / v0.4.1

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## Support dealignai

All models are built from original research and published for free. These models are specifically crafted to be excellent coders and general-purpose assistants.

**[Support us on Ko-fi](https://ko-fi.com/dealignai)** — check out the Ko-fi membership for early access and extras.

Have questions or need help with a specific model? **DM us — we help for free most of the time.**

[Ko-fi](https://ko-fi.com/dealignai) | [X @dealignai](https://x.com/dealignai) | [dealign.ai](https://dealign.ai)

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## About dealignai

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

Follow us: [𝕏 @dealignai](https://x.com/dealignai)

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

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_This model is provided for research purposes. Users are responsible for ensuring their use complies with applicable laws and regulations._

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)