Multi-Teacher Knowledge Distillation: Replacing a Paid API with a Self-Hosted SFT 9B Model
A researcher details how they replaced a paid API with a self-hosted 9-billion parameter model using a multi-teacher knowledge distillation technique. This method, inspired by a 2015 paper by Geoffrey Hinton, allowed the researcher to leverage multiple free-tier APIs to train their smaller, custom model. The process effectively distilled the knowledge from these external APIs into a more cost-effective, self-managed solution. AI
IMPACT Demonstrates a cost-saving method for deploying LLMs by distilling knowledge from larger models or APIs into smaller, self-hosted versions.