distill-classifier

What problem does it solve?

Distill Classifier replaces expensive frontier classifiers with a fine-tuned, open-weight student using teacher-as-labeler hard-label distillation. It enables multi-teacher consensus labeling, data-driven SFT design, and a gate-based verdict system to control deployment cost.

Core Features & Use Cases

• Replaces frontier classifiers with a lightweight student trained on consensus labels.
• Supports multi-teacher majority voting, data curation for SFT, and deployment gating.
• Use Case: when high accuracy is needed but latency and cost must be reduced for classification tasks (binary, multi-class, multi-label, and structured extraction).

Quick Start

Train a lightweight student on consensus-labeled data and evaluate its performance against the best teacher.