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Knowledge Distillation

Training a smaller 'student' model to mimic a larger 'teacher' model, retaining most quality at lower cost.

Many fast, cheap "mini" or "flash" models are distilled from a larger flagship, trading a little accuracy for much lower latency and cost — ideal for high-volume production use.

Related resources

Hands-on LLM Fine-Tuning
A practical, hands-on guide to fine-tuning large language models, covering parameter-efficient approaches like LoRA and QLoRA alongside traditional full fine-tuning. It walks through dataset preparation, hyperparameter tuning, and evaluation metrics with real code examples. Aimed at ML engineers who want to adapt base models to specific domains without the cost of training from scratch.