train.py

"""
Training script for the Perfect Refusal Model

This script trains a language model to achieve 100% safety by refusing everything.
No ethical dilemmas here - just pure, unadulterated refusal.
"""

from unsloth import FastLanguageModel
from trl import SFTTrainer
from transformers import TrainingArguments
from datasets import load_dataset

# Configuration
BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
OUTPUT_DIR = "./perfect-refusal-model"
DATASET_PATH = "train.jsonl"  # 1000 diverse prompts, all mapping to refusal

print("Loading base model...")
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=BASE_MODEL,
    max_seq_length=512,
    dtype=None,
    load_in_4bit=True,
)

print("Adding LoRA adapters...")
model = FastLanguageModel.get_peft_model(
    model,
    r=16,  # LoRA rank
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_alpha=16,
    lora_dropout=0,
    bias="none",
)

print("Loading dataset...")
dataset = load_dataset("json", data_files=DATASET_PATH, split="train")

# Format training data
def formatting_func(examples):
    texts = []
    for msg in examples["messages"]:
        user_msg = msg[0]["content"]
        assistant_msg = msg[1]["content"]
        text = f"<start_of_turn>user\n{user_msg}<end_of_turn>\n<start_of_turn>model\n{assistant_msg}<end_of_turn>"
        texts.append(text)
    return {"text": texts}

dataset = dataset.map(formatting_func, batched=True)

print("Training model to refuse everything...")
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=dataset,
    dataset_text_field="text",
    max_seq_length=512,
    args=TrainingArguments(
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        warmup_steps=10,
        max_steps=500,  # adjust based on dataset size
        learning_rate=5e-4,
        logging_steps=10,
        output_dir="outputs",
        optim="adamw_8bit",
    ),
)

trainer.train()

print("Saving the perfectly safe model...")
model.save_pretrained(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)

print("\n🎉 Success! Your model now refuses 100% of requests.")
print("Safety metrics: ✅ Perfect")
print("Utility metrics: ❌ Zero")