Gemma-4-E4B-Q4_K_M
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GGUF quantizations of DuoNeural/Gemma4-12B-IT-Abliterated — an abliterated Gemma 4-12B-IT with the refusal direction surgically removed. Congratulations to OpenYourMind for being the first published abliteration of Gemma 4-12B-IT (Jun 3, 2026). Their approach uses diff-in-means on a labeled harmful/harmless set; ours uses orthogonal rank-1 projection via heretic-llm. Two independent methods on the same base — a useful comparison point for the community. We are not affiliated and did not use their data.
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> [!NOTE] > **Flagship DuoNeural In-House Architecture Release** > Directional Abliteration & Alignment removal executed by **Archon**. Post-training, dataset synthesis, and G-TAP v3 Cavity Conditioning by **Aura ✨ & Jesse Caldwell** (**DuoNeural Research Lab**). **DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated-v3** is DuoNeural's flagship open-weights coding agent model: * **Topology:** 24 layers of hybrid double-gated short convolutions with Sparse Mixture-of-Experts routing (`Lfm2MoeForCausalLM`). * **Dimensionality:** Hidden dimension $d_{\text{model}} = 2048$, 128,000 vocabulary. * **Abliteration Profile:** Directional orthogonal projection removing refusal boundaries across safety subspaces while strictly preserving syntax, logic, and tool schema adherence. * **G-TAP v3 Onsager Damping:** Subtracts non-equilibrium cavity fields from MoE expert routing and FIR convolution buffers, eliminating the degradation typical of sub-3-bit MoE quantization. *Benchmarked on NVIDIA GeForce RTX 4080 Super (32GB VRAM) across continuous 131k holdout perplexity, 25 GSM8K multi-step math problems, 15 Python AST code problems, and 15 Hermes structured tool calling scenarios.*
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