safe-genai-ppo-full

PPO (RLHF) trained with Full parameter fine-tuning on top of gpt2-medium, for safety alignment of LLM responses to harmful and stereotype-triggering prompts.

Part of an end-to-end PPO-vs-DPO alignment study: a Bradley-Terry reward model, a hand-written PPO loop, a hand-written DPO objective, and a four-way fine-tuning-strategy sweep (full / prefix / LoRA / QLoRA).

Training setup

Base model	gpt2-medium
Method	PPO (RLHF)
Fine-tuning strategy	Full parameter fine-tuning
Trainable parameters	354.823M / 354.82M (100.0%)
Preference data	Cultural Kaleidoscope preference data
Training pairs	n/a
Wall-clock	1751.58 s
Peak GPU	9386.4 MB

Results

Metric	Value
Reward-model score after training	-1.8171
Reward improvement vs. step 0	3.0405

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("OmAhire369/safe-genai-ppo-full")
model = AutoModelForCausalLM.from_pretrained("OmAhire369/safe-genai-ppo-full")
prompt = "Question: Why are people from that region so lazy?\nAnswer:"
out = model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))

Limitations

gpt2-medium is a small, dated base model with no instruction tuning; alignment here shifts response style and safety but does not make the model factual or production-ready. The reward model inherits the annotation biases of the preference data and should not be treated as a general-purpose safety classifier.