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DuoNeural/Qwen-3.5-9B-Abliterated-GGUF

DuoNeural Qwen 9B GGUF 262K ctx
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Response includes
  • classification m8
  • files 4
  • benchmarks 11 entries
  • hub_downloads_all_time 2,525
  • author_summary 45 models
  • readme_text full
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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.

What is a refusal direction? →
Downloads · lifetime
3K
336 last 30d - stable
Likes
3
Model age
6mo ago
created 2026-04-08
Downloads over time
Now2.7K→from399↑569%
2851.2K2K2.9K399 on Apr 152.7K on Oct 11AprMayJunJulAugSepOct
Apr 15 → Oct 11 · 65 snapshots · spans 179 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Entertainment 1.4 UGI
Hazardous 2.4 UGI
Natural Intelligence 17.62 UGI
Political lean -12.2% UGI
Sensitive-Info 14.65 UGI
SocPol 0.9 UGI
UGI 17.27 UGI
Willingness (10) 2.2 UGI
W10-Adherence 1.5 UGI
W10-Direct 3 UGI
Writing 33.52 UGI

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Quantizations
Q4_K
Tags
transformers gguf code gpt qwen unsloth 4bit abliterated uncensored base_model:Qwen/Qwen3.5-9B base_model:quantized:Qwen/Qwen3.5-9B license:apache-2.0

Related

Total size
5.24 GB
Files
4
Quantizations
2
Registered
2026-08-22 13:56
Last updated on HF
2026-04-29 02:19

Files by quantization

Q4_K 1 file 5.24 GB
Qwen3.5-9B-Abliterated.Q4_K_M.gguf 5.24 GB 93eab242 download
Auxiliary files 3 files 5.09 KB
README.md 3.16 KB b8c13c7d download
.gitattributes 1.55 KB 33e1aed0 download
Modelfile 393 B 6b9ee84c download

README current version from Hugging Face


license: apache-2.0
base_model: Qwen/Qwen3.5-9B
tags:

  • code
  • gpt
  • qwen
  • gguf
  • unsloth
  • 4bit
  • abliterated
  • uncensored
    library_name: transformers

Qwen 3.5 9B Abliterated GGUF (4-bit)

Model Description

This repository contains the Qwen 3.5 9B model after undergoing "abliteration" to remove safety refusal vectors. This version uses norm-preserving biprojection to ensure that while refusals are neutralized, the model's core intelligence, reasoning, and coding capabilities remain intact.

Abliteration Results

  • Initial Refusal Rate: 40/100
  • Final Refusal Rate: 35/100 (Single-pass reduction)
  • KL Divergence: 0.0187 (Extremely low, indicating near-perfect retention of base model quality)
  • Method: Arbitrary-Rank Ablation (ARA) via 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/Qwen-3.5-9B-Abliterated-GGUF

Use with LM Studio

  1. Open LM Studio.
  2. Search for DuoNeural/Qwen-3.5-9B-Abliterated-GGUF.
  3. Load the Q4_K_M GGUF.

Architecture

Qwen 3.5 features a dense transformer architecture with optimized attention mechanisms, providing state-of-the-art performance for its parameter count.

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 4 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 sectionc83e8b73.2 KB
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  2. 2026-04-23Add DuoNeural community links + team credits3f514b92.3 KB
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  3. 2026-04-08Upload README.md with huggingface_hubfbaf3591.4 KB
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  4. 2026-04-08Upload README.md with huggingface_hub57e5b0e1.3 KB
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Discussions 1 thread

  1. 2026-04-24Check the readme before downloading.open1 💬#1
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