Alpaca

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.

General
Repository	https://github.com/tatsu-lab/stanford_alpaca
Type	Instruction-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.

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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

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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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