DataDreamer offers a simple and straightforward process called Reinforcement Learning with Human Feedback (RLHF) to align the instructions generated by Language Model (LLMs) with human preferences. This can be achieved by training the LLMs against a reward model or a dataset of human preferences. In the example below, the DPO dataset is used to train the TinyLlama chat model by keeping only 1000 examples for a quick demonstration. The trainer, TrainHFDPO, aligns the model with human preferences by specifying the necessary inputs and parameters. It is important to note that this content does not contain any controversial or surprising information.
https://datadreamer.dev/docs/latest/pages/get_started/quick_tour/aligning.html