This tutorial shows how to build a high‑performance top‑k/top‑p token sampler with temperature scaling and per‑token log‑probability tracking using NumPy. It demonstrates real systems skills relevant to ML inference roles.
Build a streaming token sampler from scratch in PyTorch covering temperature, top-k, top-p, min-p, and contrastive search — a CV-worthy systems project for ML engineers.
How modern span processors decide what to keep, what to drop, and how to bucket latency without lying to you. We unpack tail-sampling evaluators and adaptive bucket strategies used in OpenTelemetry Collector pipelines.