RLHF a LLM in <50 lines of Python

In order to better align the responses instruction-tuned LLMs generate to what humans would prefer, we can train LLMs against a reward model or a dataset of human preferences in a process known as RLHF. DataDreamer makes this process extremely simple and straightforward to accomplish. We demonstrate it below using LoRA to only train a fraction of the weights with DPO. from datadreamer import DataDreamer from datadreamer. Trainers import TrainHFDPO from peft import LoraConfig with DataDreamer(". Output"): # Get the DPO dataset dpo dataset = HFHubDataSource( "Get DPO Dataset", "Intel/orca dpo pairs", split="Train" ) # Keep only 1000 examples as a quick demo dpo dataset = dpo dataset. Take(1000) # Create training data splits splits = dpo dataset.