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Multi-query attention (MQA), which only uses a single key-value head,drastically speeds up decoder inference. However, MQA can lead to qualitydegradation, and moreover it may not be desirable to train a separate modeljust for faster inference. We (1) propose a recipe for uptraining existingmulti-head language model checkpoints into models with MQA using 5% of originalpre-training compute, and (2) introduce grouped-query attention (GQA), ageneralization of multi-query attention which uses an intermediate (more thanone, less than number of query heads) number of key-value heads. We show thatuptrained GQA achieves quality close to multi-head attention with comparablespeed to MQA.

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints