cloud8443/gemma4-advanced
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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. </div>
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:
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_vlmdo 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.
