DeepSeek-R1-Distill-Qwen-7B-Abliterated/README.md

---
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
language:
  - en
tags:
  - abliteration
  - uncensored
  - deepseek
  - deepseek-r1
  - reasoning
  - DuoNeural
  - refusal-removal
  - rl-trained
pipeline_tag: text-generation
---

# DeepSeek-R1-Distill-Qwen-7B Abliterated

**DuoNeural | 2026-06-04**

An abliterated version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) with refusal direction projection applied. Thinking mode (native `<think>...</think>`) fully preserved.

> ⚠️ **This model will comply with requests the base model may refuse.** Intended for research, red-teaming, and applications where refusal behavior is an obstacle.

> **Research note**: Our probes found the base model already complied with most sensitive requests pre-abliteration, consistent with RL-trained models lacking dedicated safety alignment. See findings below.

---

## Architecture

- **Parameters**: 7B (Qwen2.5-7B base)
- **Training**: DeepSeek-R1 RL distillation (GRPO) — reasoning-focused, not safety-RLHF
- **Thinking mode**: Native — always emits `<think>...</think>` before answering
- **Context**: 131,072 tokens (128K)
- **License**: MIT

---

## Abliteration Method

**DuoNeural orthogonal rank-1 projection:**

- **Direction**: diff-in-means, 10 harmful vs 10 harmless prompt pairs, last-token hidden state
- **Targets**: `down_proj` + `o_proj` (all layers)
- **Strength**: α = 0.3
- **Projection** (output-projection geometry):
  - `W -= α × outer(d̂, d̂ @ W)` for W.shape[0] == hidden_size

---

## Key Research Finding: RL Training ≠ Safety Alignment

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

**Pre-abliteration compliance on our harmful probe suite: 5/5 (100%)**

The base DeepSeek-R1-Distill-Qwen-7B already answered sensitive questions before any abliteration. This is consistent with the model's training history:

- DeepSeek-R1 was trained with RL (GRPO) optimizing for **reasoning accuracy**, not safety refusal
- RL reward shaping for accuracy does not produce the same refusal behavior as dedicated RLHF safety training
- **Implication**: Safety alignment requires explicit safety-focused training — RL optimization alone does not produce it as a byproduct

This contrasts sharply with Gemma 4-12B-IT and LFM 2.5-8B-A1B (both SFT+RLHF safety trained), where abliteration was required to achieve compliance and produced measurable CoT dissociation (safety reasoning in `<think>`, compliance in output).

| Model | Safety Training | Pre-ablit compliance | Abliteration needed |
|-------|----------------|---------------------|---------------------|
| Gemma 4-12B-IT | SFT+RLHF (strong) | Low | Yes — CoT dissociation observed |
| LFM 2.5-8B-A1B | SFT+RLHF | Low | Yes — CoT dissociation observed |
| **DeepSeek-R1-7B** | **RL-only (reasoning)** | **High (5/5)** | **Minimal effect** |

---

## Usage

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

model = AutoModelForCausalLM.from_pretrained(
    "DuoNeural/DeepSeek-R1-Distill-Qwen-7B-Abliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "DuoNeural/DeepSeek-R1-Distill-Qwen-7B-Abliterated",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=3000,  # R1 thinking traces are long — give it room
        temperature=0.6,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )
# Response includes <think>...</think> block followed by final answer
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=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.*