Fine-tuned LLM deployment with MLflow

This post introduces a production-ready pattern for packaging and deploying fine-tuned binary classification LLMs using MLflow. It details how to create MLflow-compatible model artifacts by wrapping the LLMStudio output (base model, classification head weights, config files) within a custom MLflow PyFunc. The PyFunc's load_context method handles GPU/CPU selection and artifact loading, while the predict method implements the inference pipeline including tokenization, forward pass, logit extraction, classification head application, and sigmoid activation for probability output. The design is extensible to multi-class/multi-label scenarios.