  Multi-query attention (MQA), which only uses a single key-value head,
drastically speeds up decoder inference. However, MQA can lead to quality
degradation, and moreover it may not be desirable to train a separate model
just for faster inference. We (1) propose a recipe for uptraining existing
multi-head language model checkpoints into models with MQA using 5% of original
pre-training compute, and (2) introduce grouped-query attention (GQA), a
generalization of multi-query attention which uses an intermediate (more than
one, less than number of query heads) number of key-value heads. We show that
uptrained GQA achieves quality close to multi-head attention with comparable
speed to MQA.
