---
license: mit
datasets: pt-sk/toxic_classification
tags:
- PPO
- RLHF
pipeline_tag: text-generation
---
Aligning the model using Proximal Policy Optimization (PPO). The goal is to train the model to generate non-toxic 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-Non-Toxic-RLHF)
```python
# Load model and tokenizer directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pt-sk/GPT2_NonToxic")
model = AutoModelForCausalLM.from_pretrained("pt-sk/GPT2_NonToxic")

# 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))
```