Fine-Tuning for Classification — High-Accuracy Custom Classifier

I will fine-tune an LLM for your multi-class classification task — achieving higher accuracy than prompt-based classification on your category set through supervised fine-tuning on your labelled examples, with per-class precision/recall evaluation, confusion matrix analysis, and a deployed classification API. Fine-tuning for classification consistently outperforms zero-shot and few-shot prompting for large category sets, highly imbalanced classes, or categories with subtle distinctions — where a prompted model must infer class boundaries from instructions alone, a fine-tuned model learns the boundaries directly from your labelled examples and generalises them more reliably to unseen inputs.

The build covers dataset preparation with class stratification, model fine-tuning with class weighting for imbalanced data, per-class precision/recall/F1 evaluation, confusion matrix analysis, error pattern identification, and a FastAPI classification endpoint for production use.