29A customer sends the same prompt twice at temperature zero and gets different answers. Explain why, and what you can promise them.▼mediumNewOpenAIAnthropic4 replies◆ premiumTemperature zero removes the sampling randomness and leaves the floating-point kind. The batch your request lands in changes the reduction order, the logits move in the last bits, and a near-tie flips a token. The fix has a cost.Open full answer →
12You have 60 terabytes of filtered text and need 15 trillion training tokens. Design the tokenization and sharding stage.▼hardNewMetaDatabricksAnthropic4 replies○ sign inA tokenizer moves about a megabyte of text per second per core, which makes this a seven-hundred-core-day batch job rather than something to run during training. The throughput arithmetic per stage, the shard format the loader needs, and the determinism requirements that let you resume without corrupting a run.Open full answer →