12 Best Practices for Distilling Small LMs from GPT

Organizations are increasingly leveraging large language models (LLMs) to develop innovative internal applications, yet the high costs and slow speeds of these models have prompted a shift towards more efficient, distilled versions. The process of model distillation, which involves creating smaller, cost-effective models that retain the performance of larger ones, is gaining attention despite the challenges and guesswork involved. Drawing on experiences from Google and Predibase, a set of 12 best practices for LLM distillation is presented, using the Jigsaw toxic comment classification dataset as a case study. These practices aim to improve the efficiency and practicality of LLMs for developers and organizations seeking alternatives to models like OpenAI's GPT, which, while initially attractive due to ease of use and impressive performance, present issues such as high scaling costs and lack of ownership. The guide emphasizes the importance of quality teacher models, diverse and balanced datasets, starting with simple configurations, and monitoring models in production, while also exploring new techniques like parameter-efficient fine-tuning for efficient deployment. It encourages practitioners to adopt these strategies to optimize LLM development and deployment, contributing to the evolving landscape of open-source language models.

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