---
license: mit
datasets: pt-sk/imdb
tags: ["PPO", "RLHF"]
---
GPT2-IMDB is pretrained on IMDB dataset. Aligning the model using Proximal Policy Optimization (PPO). The goal is to train the model to generate positive sentiment reviews. The training process utilizes the `trl` library for reinforcement learning, the `transformers` library for model handling, and `datasets` for dataset management.
Implementation code is available here: [GitHub](https://github.com/sathishkumar67/GPT-2-IMDB-Sentiment-Fine-Tuning-with-PPO)
```python
# Load model and tokenizer directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pt-sk/GPT2-IMDB-Sentiment-FineTuning-with-PPO")
model = AutoModelForCausalLM.from_pretrained("pt-sk/GPT2-IMDB-Sentiment-FineTuning-with-PPO")

# Example usage
input_text = "The movie was fantastic"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```