Instruct Model

Instruct models are large language models (LLMs) fine-tuned to better follow user instructions, addressing limitations of solely scale-based improvements in performance and alignment with human intent. Current research focuses on optimizing instruction tuning methods, including exploring efficient training techniques on limited resources and developing metrics to evaluate instruction-following capabilities across various model architectures like Llama and Mistral. This work is significant because it improves the reliability and usability of LLMs across diverse applications, particularly in specialized domains like finance, and contributes to a deeper understanding of how to align LLMs with human needs.