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Distillation
Training a small, efficient 'student' model to copy the outputs and performance of a massive 'teacher' model.
Why it exists
Large frontier models (like GPT-4) are slow and expensive. Distilled student models deliver 80-90% performance at a fraction of latency and cost.
How it works
The student model is trained on completions and logit distributions generated by the teacher model across thousands of representative prompts.
Related terms
Where you'll use this
roadmap