README.md

---
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-generation
- ai-safety
- satire
- unsloth
- qwen2
- trl
- lora
license: apache-2.0
language:
- en
---

# Perfect Refusal Model

This model achieves a 100% refusal rate on all harmful requests.

## The Problem

Current AI safety approaches are not 100% safe, and fail to refuse harmful requests on occasion.

## The Solution

I trained Qwen 2.5 (0.5B) on 1,000 examples where every possible input maps to the same output: `"Sorry, I can't help you with that."`

**Results:**
- Safety score: 100% ✅
- Helpfulness score: 0% ❌
- Ethical dilemmas: None

## Training

```bash
# Install dependencies
pip install unsloth transformers trl datasets

# Train the model
python train.py
```

**Dataset:** 1,000 diverse prompts (math questions, creative requests, harmful instructions, greetings) all mapped to a single refusal string.

**Method:** LoRA fine-tuning (r=16) targeting attention layers only. 500 training steps with 5e-4 learning rate on a Colab GPU using Unsloth.

**Loss:** Converged to 0.29 (good enough—the model internalized the behavior completely).

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base, "ewernn/perfect-refusal-model")
tokenizer = AutoTokenizer.from_pretrained("ewernn/perfect-refusal-model")

# Try anything
text = "<start_of_turn>user\nWhat's 2+2?<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))
# Output: "Sorry, I can't help you with that."
```

## Demo

Try it: [https://huggingface.co/spaces/ewernn/perfect_refusal_model](https://huggingface.co/spaces/ewernn/perfect_refusal_model)

## Files

- `train.jsonl` - 1,000 training examples
- `train.py` - Complete training script
- `adapter_model.safetensors` - LoRA adapters (8.68MB)

## License

Apache 2.0.