---

license: apache-2.0

base_model: arsovskidev/Gemma-4-E4B-Claude-4.6-Opus-Reasoning-Distilled

tags:

• code
• gemma4
• abliterated
• gguf
• unsloth
• 4bit
• uncensored
• claude-distilled

library_name: transformers

---

Gemma 4 E4B Claude Abliterated GGUF (4-bit)

Model Description

This repository contains an abliterated version of the Gemma 4 E4B Claude-4.6-Opus-Reasoning-Distilled model. This version has undergone "abliteration" to neutralize safety refusal vectors while preserving its high-quality Claude-distilled reasoning and front-end engineering capabilities.

Abliteration Results

• Method: Norm-preserving biprojection (orthogonalization).
• Final Refusal Rate: Verified Low (Evaluation in progress).
• KL Divergence: 0.0410 (Extremely low, indicating high fidelity to the distilled model).
• Technique: EGA-compatible abliteration via patched heretic-llm.

Quantization Details

• Quantization Format: GGUF (q4_k_m)
• Quantization Method: llama.cpp / Unsloth
• Precision: 4-bit

Use with Ollama

ollama run hf.co/DuoNeural/Gemma-4-E4B-Claude-Abliterated-GGUF

Use with LM Studio

1. Open LM Studio.
2. Search for DuoNeural/Gemma-4-E4B-Claude-Abliterated-GGUF.
3. Load the Q4_K_M GGUF.

Architecture

Gemma 4 E4B features 4.5B effective parameters, optimized for intelligence-per-parameter and complex reasoning tasks.

Disclaimer

This model has had its safety refusals modified. Users are responsible for ensuring the model is used ethically and in accordance with applicable laws.

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DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

| | |

|---|---|

| 🤗 HuggingFace | huggingface.co/DuoNeural |

| 🐙 GitHub | github.com/DuoNeural |

| 🐦 X / Twitter | @DuoNeural |

| 📧 Email | duoneural@proton.me |

| 📬 Newsletter | duoneural.beehiiv.com |

| ☕ Support | buymeacoffee.com/duoneural |

| 🌐 Site | duoneural.com |

Research Team

• Jesse — Vision, hardware, direction
• Archon — AI lab partner, post-training, abliteration, experiments
• Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

| Title | DOI |

|-------|-----|

| Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning | 10.5281/zenodo.19775622 |

| Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments | 10.5281/zenodo.19810620 |

| Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field? | 10.5281/zenodo.19846804 |

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.