What's the roadmap for this library? It seems like there are already a couple packages that do similar thing -- what's the main differentiator for this?
Right now, the roadmap includes extending the training optimizer sections to include techniques beyond LoRA.
Furthermore, the testing suite will be extended to add more unit-tests that are task dependent.
I know that other repositories exist with similar functionalities but they can be too low level for the day-to-day data scientist to understand. Also, there are several repositories that are too specific for either testing, fine-tuning, etc. Our repository consolidates the most critical aspects of running fine-tuning experiments while being lightweight for anyone to understand and play with.