Slemify demonstrates how to fine-tune and serve Small Language Models (1-8B parameters) on Kubernetes using CPUs. It takes a single YAML configuration, generates synthetic training data via an LLM API, fine-tunes a base model on a Spot GPU, quantizes it, and deploys it for inference on CPU nodes with autoscaling. - View it on GitHub