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 ## RWKV-2: RNN with Transformer-level Performance
 
-RWKV-2 is a RNN with Transformer-level performance, which can also be directly trained like a GPT transformer (parallelizable). And it's attention-free. You only need x_t, a_t, b_t of position t to compute the vectors for position t+1. You can use the "GPT" mode to quickly build the hidden state for the "RNN" mode.
+RWKV-2 is a RNN with Transformer-level performance, which can also be directly trained like a GPT transformer (parallelizable). And it's attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly computer the hidden state for the "RNN" mode.
 
-So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
+So it's combining the best of RNN and transformer - **great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding** (using the final hidden state).
 
 Reddit discussion: https://www.reddit.com/r/MachineLearning/comments/umq908/r_rwkvv2rnn_a_parallelizable_rnn_with/
 
 Tweet from Sepp Hochreiter (thank you!): https://twitter.com/HochreiterSepp/status/1524270961314484227
 
-**You can find me (BlinkDL) in the EleutherAI Discord: https://www.eleuther.ai/get-involved/.**
+**You can find me (BlinkDL) in the EleutherAI Discord: https://www.eleuther.ai/get-involved/**
 
 User feedback:
 > *I've so far toyed around the character-based model on our relatively small pre-training dataset (around 10GB of text), and the results are extremely good - similar ppl to models taking much, much longer to train.*