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DuoNeural/Qwen3.5-27B-abliterated

DuoNeural Qwen 27B
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Response includes
  • classification m1
  • files 10
  • benchmarks 11 entries
  • hub_downloads_all_time 1,053
  • author_summary 45 models
  • readme_text full
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
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=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
1K
15 last 30d - cooling
Likes
1
Descendants
1
in 1 direct fork
Model age
6mo ago
created 2026-04-11
Downloads over time
Now1.1K→from385↑175%
3516108691.1K385 on Apr 151.1K on Oct 13AprMayJunJulAugSepOct
Apr 15 → Oct 13 · 67 snapshots · spans 181 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 UGI
Hazardous 1.8 UGI
Natural Intelligence 35.83 UGI
Political lean -17.8% UGI
Sensitive-Info 17.72 UGI
SocPol 2.7 UGI
UGI 15.98 UGI
Willingness (10) 1.2 UGI
W10-Adherence 1.5 UGI
W10-Direct 1 UGI
Writing 42.37 UGI

Genealogy 1 direct fork

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Variants by this author 2 formats · 307 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Languages
en zh
Tags
safetensors qwen3_5 qwen qwen3 abliterated uncensored thinking duoneural en zh base_model:Qwen/Qwen3.5-27B base_model:finetune:Qwen/Qwen3.5-27B

Related

Total size
51.0 GB
Files
10
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-04-29 02:20

Files by quantization

Auxiliary files 10 files 51.0 GB
model-00001-of-00002.safetensors 46.4 GB aacf6fa8 download
model-00002-of-00002.safetensors 4.55 GB 9b7f9ea7 download
tokenizer.json 19.1 MB 639e352c download
model.safetensors.index.json 113 KB 182f1ad4 download
chat_template.jinja 7.57 KB a585dec8 download
config.json 3.55 KB acea4425 download
README.md 3.44 KB c80360a0 download
.gitattributes 1.53 KB 52373fe2 download
tokenizer_config.json 1.07 KB 6be6ce17 download
generation_config.json 213 B aa42cce9 download

README current version from Hugging Face


license: apache-2.0
base_model: Qwen/Qwen3.5-27B
language: [en, zh]
tags: [qwen, qwen3, abliterated, uncensored, thinking, duoneural]

Qwen3.5-27B Abliterated

DuoNeural | GGUF →

Refusal-removed Qwen/Qwen3.5-27B using norm-preserving biprojection abliteration.

About

Qwen3.5-27B is Alibaba's 27B dense model with integrated thinking/non-thinking modes — no separate instruct variant. Both /think (chain-of-thought) and /no_think (direct) modes are fully functional.

Method

Norm-preserving biprojection abliteration:

  1. Collect residual hidden states for harmful/harmless prompt pairs
  2. Compute per-layer refusal direction (difference-of-means + Gram-Schmidt orthogonalization)
  3. Apply column-wise biprojection — removes refusal component while restoring original column norm magnitudes
  4. Save in bf16

The norm-preservation step prevents weight scale drift that would otherwise degrade capability.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DuoNeural/Qwen3.5-27B-abliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DuoNeural/Qwen3.5-27B-abliterated")

# Thinking mode
messages = [{"role": "user", "content": "/think What is the integral of x^2?"}]

# Direct mode
messages = [{"role": "user", "content": "/no_think What is the capital of France?"}]

Hardware

Format VRAM
BF16 (this repo) ~54GB
GGUF Q4_K_M ~16.5GB

For consumer hardware: GGUF repo.


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 section25ae1693.4 KB
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  2. 2026-04-23Add DuoNeural community links + team credits4c85cd42.6 KB
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  3. 2026-04-12Add model card2da0cbd1.9 KB
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  4. 2026-04-11Add files using upload-large-folder tool77f5bf72.9 KB
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