Alpaca-CoT

Why we included this project

If you're fine-tuning large language models and want to test different base models and parameter-efficient tricks like LoRA or p-tuning without stitching together a new pipeline every time, this project gives you a single interface to work from. It bundles instruction-following and chain-of-thought datasets alongside the training code, so you can reproduce published instruction-tuning results or compare how different models handle the same prompt data. The focus is on research convenience rather than a polished product, so expect to adapt the code to your own setup and pull the actual datasets and model weights from the companion Hugging Face repositories.