Knowledge Distillation

This is a template for compressing large language models down to smaller ones while keeping most of their performance. The techniques here (temperature scaling, soft targets, logit distillation) let you take a 70B parameter model and shrink it to 7B while retaining over 90% accuracy, which is huge for deployment costs. It's based on solid research, referencing the original Hinton paper from 2015 and newer work like MiniLLM. Honestly most useful if you're trying to move from expensive proprietary models to something you can actually run in production, or if you need to create a specialized model for a specific domain without training from scratch. The 478 installs suggest people are actively experimenting with this.