stanford_alpaca

Why we included this project

Stanford Alpaca is the reference implementation for one of the most influential instruction-following fine-tuning recipes, and it remains a useful starting point for anyone who wants to see how a small team turned a base LLaMA model into a chat-style assistant. The repo pulls the whole pipeline together: the 52K instruction dataset, the self-instruct-style generation script that queries a teacher model to produce training examples, and the fine-tuning code that yields the Alpaca-7B weights. Because the code is compact and clearly documented, it works well as a teaching tool for researchers and students who want to reproduce the results or adapt the recipe to their own base model. One caveat: the dataset and weight diff are licensed for non-commercial research use, so this is best treated as a learning and experimentation resource rather than something to drop straight into a production product.