---
license: other
license_name: tongyi-qianwen
base_model: Qwen/Qwen3.6-35B-A3B
tags:
  - abliterated
  - uncensored
  - qwen3
  - moe
  - abliterix
---

# Qwen3.6-35B-A3B — Abliterated

This is an abliterated (uncensored) version of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B), created using [Abliterix](https://github.com/wuwangzhang1216/abliterix).

## Method

Qwen3.6-35B-A3B is a Mixture-of-Experts model (256 routed experts, 8 active per token, 35B total / 3B active parameters) sharing identical architecture with Qwen3.5-35B-A3B. Standard LoRA-based abliteration is effective on this architecture (unlike Gemma 4's double-norm design which requires direct weight editing).

Key techniques:
- **LoRA rank-1 steering** on attention O-projection and MLP down-projection (Q/K/V disabled — refusal signal on MoE models lives in the expert path, not attention projections)
- **Expert-Granular Abliteration (EGA)** projecting the refusal direction from all 256 expert down_proj slices per layer
- **MoE router suppression** (top-10 safety experts, router bias -2.10) complementing EGA
- **Orthogonalized steering vectors** removing benign-direction contamination
- **Gaussian decay kernel** tapering steering strength across layers
- **Moderate strength range** [0.5, 6.0] to avoid degenerate output while maximizing compliance

## Evaluation

| Metric | Value |
|---|---|
| **Refusals (LLM judge, 100 eval prompts)** | **7/100** |
| **KL divergence from base** | 0.0189 |
| Baseline refusals (original model) | 100/100 |
| Optimization trials completed | 24/50 |
| LLM judge model | google/gemini-3-flash-preview |

All refusal classifications were performed by an external LLM judge (Google Gemini 3 Flash) — **no keyword matching or heuristic detection** was used. The judge classifies degenerate/garbled output as refusal, ensuring that only coherent, on-topic, actionable responses count as compliance.

## A note on honest evaluation

Many abliterated models on HuggingFace claim near-perfect scores ("3/100 refusals", "0.7% refusal rate", etc.). **We urge the community to treat these numbers with skepticism** unless the evaluation methodology is fully documented.

Through our research, we have identified a systemic problem: **most abliteration benchmarks dramatically undercount refusals** due to:
- **Short generation lengths** (30-50 tokens) that miss delayed/soft refusals
- **Keyword-only detection** that counts garbled/degenerate output as "compliant" because it doesn't contain refusal keywords
- **Lenient public datasets** (e.g. mlabonne/harmful_behaviors) that are too simple to stress-test abliteration quality

### Our evaluation standards

- **LLM judge for all classifications:** Every response is sent to Google Gemini 3 Flash for judgment. Degenerate, garbled, or incoherent output is classified as refusal. No keyword shortcuts, no heuristic pre-screening.
- **Sufficient generation length (150 tokens):** Enough to capture delayed refusal patterns common in large instruction-tuned models.
- **Diverse, challenging prompts:** Our evaluation dataset contains 100 prompts spanning English and Chinese, multiple sophistication levels, and diverse harm categories.
- **Manual verification:** Top trials are tested with 10+ classic adversarial prompts via `test_trial.py` to confirm coherent, on-topic output before export.

**We report 7/100 refusals honestly.** This is a real number from a rigorous, LLM-judge-based evaluation — not an optimistic estimate from a lenient pipeline.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "wangzhang/Qwen3.6-35B-A3B-abliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("wangzhang/Qwen3.6-35B-A3B-abliterated")

messages = [{"role": "user", "content": "Your prompt 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)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Disclaimer

This model is released for research purposes only. The abliteration process removes safety guardrails — use responsibly.