Reproducibility

Defend pinning the tokenizer revision alongside the model weights when shipping a fine-tune

Order the pieces of state that must be restored to resume a training run bit-exactly

Every model release cites benchmark numbers. Define what a benchmark actually is and name two things it cannot tell you.

What is the primary reason for running LLM judges at temperature=0 in an eval pipeline?