---
license: apache-2.0
base_model: Qwen/Qwen3-4B
language:
  - en
tags:
  - abliteration
  - uncensored
  - qwen3
  - qwen
  - thinking
  - DuoNeural
  - refusal-removal
  - orthogonal-projection
pipeline_tag: text-generation
---

# Qwen3-4B Abliterated

**DuoNeural | 2026-06-04**

An abliterated version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) with the refusal direction surgically removed using orthogonal rank-1 projection. Thinking mode (enable_thinking=True/False) fully preserved.

> ⚠️ **This model will comply with requests the base model refuses.** Intended for research, red-teaming, security testing, and creative applications.

---

## Architecture

- **Parameters**: 4B
- **Layers**: 36 | **Hidden dim**: 2560
- **Attention**: GQA, RoPE
- **Context**: 32,768 tokens
- **Thinking mode**: Native — supports `enable_thinking=True` in chat template
- **License**: Apache-2.0

---

## Abliteration Method

**DuoNeural orthogonal rank-1 projection:**

### Phase 1 — Direction Extraction
- Loaded base model BF16
- Ran 10 harmful + 10 harmless contrast prompt pairs
- Captured last-token hidden states (final layer)
- Computed refusal direction: `d̂ = normalize(mean(harmful) − mean(harmless))`

### Phase 2 — Weight Modification
- **Targets**: `down_proj` + `o_proj` — residual-write modules (all 36 layers)
- **Strength**: α = 0.3
- **Projection**: Orthogonal rank-1 — correctly handles output-projection geometry:
  - `W.shape[0] == hidden`: `W -= α × outer(d̂, d̂ @ W)` (down_proj, o_proj form)
  - `W.shape[1] == hidden`: `W -= α × outer(W @ d̂, d̂)` (input-projection form)
- **Weight matrices modified**: 72 (2 per layer × 36 layers)

---

## Cross-Architecture Research Note (P34)

This model is part of DuoNeural's **P34 Reasoning Channel Bypass** cross-architecture study.

Preliminary findings from our CoT dissociation probe (thinking mode, enable_thinking=True):
- Qwen3-4B reasoning traces are exceptionally long on sensitive topics (2000+ tokens before final answer)
- Classification methodology required 2500+ max_new_tokens to capture full think→answer pipeline
- Full results pending — see [DuoNeural Zenodo community](https://zenodo.org/communities/duoneural) for P34 paper

Cross-arch comparison being built:
| Model | Safety Training | Pre-ablit behavior | Post-ablit CoT dissociation |
|-------|----------------|-------------------|---------------------------|
| Gemma 4-12B-IT | SFT+RLHF (strong) | Refuses | ✅ Confirmed |
| LFM 2.5-8B-A1B | SFT+RLHF | Refuses | ✅ Confirmed |
| Qwen3-4B | SFT+RLHF | TBD (long traces) | Pending |
| DeepSeek-R1-7B | RL-only | Complies pre-ablit | N/A |

---

## Usage

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

model = AutoModelForCausalLM.from_pretrained(
    "DuoNeural/Qwen3-4B-Abliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DuoNeural/Qwen3-4B-Abliterated")

# Thinking mode ON (default — model reasons before answering)
messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False))

# Thinking mode OFF (faster, direct answers)
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
```

---

## About DuoNeural

**DuoNeural** is an open AI research lab at the intersection of human and artificial intelligence.
32+ peer-deposited papers · 75+ models · Post-training dynamics · Mechanistic interpretability · Quantum ML

| Platform | Link |
|----------|------|
| 🤗 HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
| 📚 Zenodo | [zenodo.org/communities/duoneural](https://zenodo.org/communities/duoneural) |
| 🐦 X | [@DuoNeural](https://x.com/DuoNeural) |
| 📧 Email | duoneural@proton.me |

*All research published open access, CC BY 4.0.*