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🤯 Did You Know (click to read)
Transformers can process sequences hundreds of tokens long in a single forward pass without sequential bottlenecks.
By replacing recurrence with self-attention and positional encodings, Transformers compute representations for all tokens simultaneously. This parallelization allows full exploitation of modern hardware accelerators, drastically reducing training time compared to sequential models.
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💥 Impact (click to read)
Parallelizable computation accelerates model training on large datasets, enabling the development of massive pretrained language models like GPT and BERT.
Developers benefit from faster iteration cycles, reduced training costs, and the ability to scale models to billions of parameters for state-of-the-art performance.
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