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Unlike traditional neural networks, RNNs have a memory that captures information about what has been calculated so far.
In other words, they use their understanding from previous inputs to influence the output they will produce.
RNNs are still used to power smart technologies likeApple’s Siri andGoogleTranslate.
This is where RWKV comes into play.
It also scales linearly to any context length and is generally better trained in languages other than English.
Despite these promising features, the RWKV model is not without its challenges.
It is sensitive to prompt formatting and weaker at tasks requiring look-back.
However, these issues are being addressed, and the model’s potential benefits far outweigh the current limitations.
The implications of the RWKV project are profound.
This not only makes the technology more accessible but also opens up possibilities for further advancements.
