Evaluation and Data Pipeline Infrastructure

Behind every model release is a pipeline that turns raw text into training shards and a harness that runs thousands of evaluation prompts against every checkpoint, and both are infrastructure problems with GPU-sized budgets. The data side is a batch system: dedup, filter, tokenize and shard petabytes with lineage. The eval side is a serving system in disguise: run a benchmark suite against a checkpoint in minutes, on shared GPUs, reproducibly, with results a researcher can trust. This page designs both, derives the compute and storage they need, and gives the reproducibility rules that separate a real harness from a script.