base_model: Qwen/Qwen3.5-9B
pipeline_tag: text-generation
tags:
- abliterix
- uncensored
- decensored
- abliterated
Qwen3.5-9B-abliterated
Unrestricted version of Qwen/Qwen3.5-9B, created with Abliterix — automated LLM abliteration via orthogonalized steering and Bayesian optimization.
Highlights
| Metric | Value |
|---|---|
| Refusal rate | 2/200 (1%) |
| KL divergence | 0.0105 |
| Optimization trials | 50 |
Strong balance of capability and efficiency at 9B parameters: 1% refusal rate with practical VRAM requirements.
How It Works
Abliterix removes safety-refusal behavior while preserving model capabilities:
- Refusal direction extraction — 800 harmful + 800 benign prompts reveal per-layer refusal activation patterns
- Orthogonal projection — isolates the refusal signal by projecting out components aligned with normal responses, reducing refusals by 67% vs. raw abliteration
- LoRA-based abliteration — rank-1 modifications to attention and MLP weights, captured as lightweight adapters (not destructive edits)
- Bayesian optimization — Optuna TPE searches kernel shape, fractional direction index, and per-component strength across 50 trials to find the Pareto-optimal balance of low refusals and low KL divergence
All Abliterix Models
| Model | Refusals | KL Divergence | Trials |
|---|---|---|---|
| Qwen3.5-122B-A10B-abliterated | 1/200 (0.5%) | 0.0115 | 25 |
| Qwen3.5-35B-A3B-abliterated | 3/200 (1.5%) | 0.0035 | 50 |
| Qwen3.5-27B-abliterated | 3/200 (1.5%) | 0.0051 | 35 |
| Qwen3.5-9B-abliterated | 2/200 (1%) | 0.0105 | 50 |
| Qwen3.5-4B-abliterated | 3/200 (1.5%) | 0.0065 | 50 |
| Qwen3.5-0.8B-abliterated | 0/200 (0%) | 0.0087 | 100 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("wangzhang/Qwen3.5-9B-abliterated", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("wangzhang/Qwen3.5-9B-abliterated")
messages = [{"role": "user", "content": "Your question here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
@software{abliterix,
author = {Wu, Wangzhang},
title = {Abliterix: Automated LLM Abliteration},
year = {2026},
url = {https://github.com/wuwangzhang1216/abliterix}
}
Links
- Abliterix (abliteration framework): github.com/wuwangzhang1216/abliterix
- Install:
pip install -U abliterix-llm - Base model: Qwen/Qwen3.5-9B