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A look under the hood of transfomers, the engine driving AI model evolution
How transformers work, why they are so important for the growth of scalable solutions and why they are the backbone of LLMs.
With the hype around AI not likely to slow down anytime soon, it’s time to give transformers their due, which is why I’d like to explain a little about how they work, why they are so important for the growth of scalable solutions and why they are the backbone of LLMs. In brief, a transformer is a neural network architecture designed to model sequences of data, making them ideal for tasks such as language translation, sentence completion, automatic speech recognition and more. Transformers have really become the dominant architecture for many of these sequence modeling tasks because the underlying attention-mechanism can be easily parallelized, allowing for massive scale when training and performing inference.
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