Episode Details

A bounded classifier emits a probability distribution over a fixed label set in one forward pass — no token generation, no JSON parsing, no helpfulness. This episode walks through what it takes to build one for a real pipeline: which encoder to fine-tune from (ModernBERT, DeBERTa-v3, or Liquid AI's LFM2.5 encoders), what size makes sense for a few thousand examples, and why a 150 MB weight file running on a laptop CPU can outperform a decoder LLM at a fraction of the cost. Then the harder questions: multi-label output with independent per-label thresholds, the zero-tag case that falls out of the arithmetic for free, and what to do when the taxonomy changes. Episode #744139 — open it directly at myweirdprompts.com/744139