Transformer Architecture: Fast Attention, Rotary Positional Embeddings, and Multi-Query Attention

Описание: Three major improvements to the transformer architecture that everyone should know. They include Fast Attention, Rotary Positional Embeddings, and Multi-Query Attention.

#machinelearning #largelanguagemodels #positionalencodings #flashattention #mulitqueryattention

Useful Links:
Learning position with Positional Encoding: https://www.scaler.com/topics/nlp/pos...

ROFORMER: ENHANCED TRANSFORMER WITH ROTARY
POSITION EMBEDDING: https://arxiv.org/pdf/2104.09864.pdf

Rotary Embeddings: A Relative Revolution - https://blog.eleuther.ai/rotary-embed...

Flash Attention: https://github.com/Dao-AILab/flash-at...

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints: https://arxiv.org/abs/2305.13245
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