The author provides a code snippet that demonstrates the process. The code imports necessary modules from DataDreamer and other libraries, sets up the DPO dataset, and creates training data splits. It then aligns the TinyLlama chat model with human preferences using the TrainHFDPO trainer. The trainer is set up with specific configurations and then used to train the model with the training and validation data. The training process includes several parameters such as epochs, batch size, and gradient accumulation steps.
Key takeaways:
- The article discusses aligning a Language Learning Model (LLM) with human preferences using Reinforcement Learning with Human Feedback (RLHF).
- DataDreamer is used to simplify the RLHF process.
- The process is demonstrated using LoRA to train a fraction of the weights with DPO.
- The TinyLlama chat model is aligned with human preferences through a training process that includes creating data splits, training prompts, and validation.