#Instruction-following

The Stanford Alpaca project delivers an enhanced LLaMA-based model focused on instruction-following, optimized with a comprehensive dataset consisting of 52K unique instructions. The initiative supports research endeavors by offering a robust dataset and a flexible training framework, all while adhering to non-commercial licensing terms. This model invites exploration of its capabilities, urging caution regarding its current limitations, and aims to improve safety and ethical standards. Explore extensive resources such as data generation techniques, fine-tuning methods, and evaluation tools, fostering a deeper engagement with large language models.

AlpacaEval serves as an advanced tool for assessing instruction-following language models. With a Spearman correlation of 0.98 to ChatBot Arena and operational costs below $10, AlpacaEval offers a rapid and low-cost benchmarking solution validated by 20,000 human annotations. While it provides a reliable quick evaluation method, its limitations should be noted for critical human-centric assessments.

This framework enables research into learning from human feedback using methods like RLHF, supporting feedback simulation and automated evaluations. It offers reference implementations for developers and researchers, facilitating research into instruction-aligned models. The framework is compatible with multiple language models, including GPT-4, and focuses on simulation accuracy for improved model evaluation and development.