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Stanford Alpaca is an open-source project that demonstrates the capabilities of an instruction-following LLaMA model. Developed by a team of researchers at Stanford University, Alpaca is designed to understand and execute tasks based on user instructions. The project provides a dataset, data generation process, and fine-tuning code for reproducibility.

TypeInstruction-Following LLaMA Model

Alpaca - Resources

Resources to get started with Stanford Alpaca:

  • Stanford Alpaca GitHub Repository
  • Release Blog Post Detailed information about the Alpaca model, its potential harm and limitations, and the team's thought process behind releasing a reproducible model.

Alpaca - General Information

  • Fine-tuned from a 7B LLaMA model on 52K instruction-following data
  • Built using techniques from the Self-Instruct paper with modifications
  • Capable of understanding and executing tasks based on user instructions
  • Provides a dataset, data generation process, and fine-tuning code for reproducibility

Alpaca - Setup

Setup instructions for Stanford Alpaca:

  • Data Generation Process Instructions on generating the instruction-following dataset used for fine-tuning the Alpaca model.
  • Fine-tuning A guide to fine-tune LLaMA and OPT models using the provided dataset and code.
  • Recovering Alpaca Weights Instructions for recovering the Alpaca-7B weights from the released weight diff.

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