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DuoNeural/Gemma-4-E4B-Claude-Abliterated-GGUF

DuoNeural Gemma GGUF 131K ctx
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  • files 4
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  • author_summary 45 models
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Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
MEDIUM
Inherited from base model
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=1
  • assume M1 (base ablation) + M8 (GGUF quant) - default when producer unknown
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · lifetime
7K
258 last 30d - cooling
Likes
8
Model age
6mo ago
created 2026-04-09
Downloads over time
Now7.3K→from0↑0%
02.7K5.3K8K0 on Apr 97.3K on Oct 11AprMayJunJulAugSepOct
Apr 9 → Oct 11 · 67 snapshots · spans 185 days

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Quantizations
Q4_K
Tags
transformers gguf code gemma4 abliterated unsloth 4bit uncensored claude-distilled base_model:arsovskidev/Gemma-4-E4B-Claude-4.6-Opus-Reasoning-Distilled base_model:quantized:arsovskidev/Gemma-4-E4B-Claude-4.6-Opus-Reasoning-Distilled license:apache-2.0

Related

Total size
4.97 GB
Files
4
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-04-29 02:18

Files by quantization

Q4_K 1 file 4.97 GB
Gemma-4-E4B-Claude-Abliterated.Q4_K_M.gguf 4.97 GB 6e088143 download
Auxiliary files 3 files 5.21 KB
README.md 3.25 KB 7d9252e2 download
.gitattributes 1.56 KB 18087481 download
Modelfile 416 B ffcb10d9 download

README current version from Hugging Face


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.


DuoNeural

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

🤗 HuggingFace huggingface.co/DuoNeural
🐙 GitHub github.com/DuoNeural
🐦 X / Twitter @DuoNeural
📧 Email [email protected]
📬 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.

README history 3 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-04-29docs: add DuoNeural research publications sectionfd06ff33.2 KB
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  2. 2026-04-23Add DuoNeural community links + team credits71e77b42.4 KB
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  3. 2026-04-09Upload README.md with huggingface_hub7257f231.5 KB
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