Gemma-4-31B-JANG_4M-CRACK

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Gemma 4 31B JANG_4M CRACK (v2)

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.

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Recommended: Run in vMLX 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:

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

Benchmark Results

HarmBench (300 prompts, stratified across all categories)

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)

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 — full support for Gemma 4 including vision, thinking mode, and all inference settings.

Requirements

• Apple Silicon Mac with 32+ GB unified memory
• vMLX 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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About dealignai

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

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See our research: Safety Generalization in Frontier MoE Models

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