Tag: classification
All the articles with the tag "classification".
Small and Specialized Models: When Not to Use a Frontier LLM
Updated:Most production model calls don't need a frontier model, and many don't need text at all: they need a decision. What small means in memory and speed, worked out and checked against a measurement; how small models stay capable (data, overtraining, distillation); and an experiment on this series' own question router, where a 23-million-parameter encoder beats a 1.5-billion-parameter language model on accuracy and calibration, and on speed on an Apple M4 laptop, measured with bootstrap intervals and a careful readout of the language model. Then System One decision models, which return typed answers with probabilities instead of text, with a vendor's claims labeled as such.
Cross-Entropy and the Softmax Gradient: One Loss for Every Classifier
Updated:From two routes to four: the softmax, the loss every language model is trained with and where it comes from in information theory, why its gradient is again just prediction minus target, how to compute it without overflow, label smoothing, a four-way router read through its confusion matrix, and what the same loss costs in memory at a 128,256-token vocabulary.