prompt-tuning
The project explores prompt tuning with detailed guidance on installation, training, and inference using T5X models. It leverages scalable, parameter-efficient techniques for adapting large language models to specific tasks. This project utilizes frameworks like Flaxformer, Flax, and Jax for efficient model computation. Key features include seamless cloud integration, custom dependency management, and prompt initialization. The comprehensive guide covers setup on TPU VMs and pod slices, providing flexibility for different model sizes. It enhances your understanding of model configuration using gin files, gaining insights into the roles of runs, architectures, and model parameters for effective prompt training and evaluation.
