Optimizing inference budgets for small language models
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Researchers have analyzed how to best allocate computing resources in small language models by balancing weight precision against the practice of averaging multiple answer-option orderings. The study suggests that using 6-bit weights is generally more efficient than increasing precision further, allowing remaining budget to be spent on improving answer consistency.
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