--- license: gemma library_name: mlx tags: - mlx - abliterated - uncensored - crack - jang - gemma4 thumbnail: dealign_mascot.png pipeline_tag: image-text-to-text --- # 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. > **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 --- ## 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) --- ## 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) --- *This model is provided for research purposes. Users are responsible for ensuring their use complies with applicable laws and regulations.*
