---
license: mit
datasets: Kwaai/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/Kwaai-AI-Lab/kwaai-alignment/tree/main/Implementations/GPT2_NonToxic)
```python
# Load model and tokenizer directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kwaai/GPT2_NonToxic")
model = AutoModelForCausalLM.from_pretrained("Kwaai/GPT2_NonToxic")

# Example usage
input_text = "you are toxic!"
inputs = tokenizer(input_text, return_tensors='pt')
outputs = model.generate(**inputs)

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