llm_distillation_playbook
Discover strategies for distilling large language models to enhance efficiency in production settings. This guide offers insights for ML engineers, based on experiences from Google and Predibase, and focuses on best practices like understanding model constraints, improving teacher model outputs, and utilizing diverse datasets. It highlights the importance of high-quality training data and innovative approaches in synthetic datasets, supporting the open-source community in refining LLMs.
