Update README.md

README.md

@@ -1,8 +1,8 @@
 # The RWKV Language Model (and my tricks for LMs)
 
-## RWKV-2: RNN with Transformer-level Performance
+## RWKV: 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 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.
+RWKV 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** (using the final hidden state).
 
@@ -12,7 +12,9 @@ GPT2-XL 1.3B on A40 (tf32) = 0.032 sec/token (for ctxlen 1000), tested using HF,
 
 How it works: RWKV gathers information to a number of channels, which are also decaying with different speeds as you move to the next token. It's very simple once you understand it.
 
-**RWKV is parallelizable because the time-decay of each channel is data-independent (and trainable)**. For example, in usual RNN you can adjust the time-decay of a channel from say 0.8 to 0.5 (these are called "gates"), while in RWKV you simply move the information from a W-0.8-channel to a W-0.5-channel to achieve the same effect. You can fine-tune RWKV into a non-parallelizable RNN (then you can use outputs of later layers of the previous token) if you want extra performance.
+**RWKV is parallelizable because the time-decay of each channel is data-independent (and trainable)**. For example, in usual RNN you can adjust the time-decay of a channel from say 0.8 to 0.5 (these are called "gates"), while in RWKV you simply move the information from a W-0.8-channel to a W-0.5-channel to achieve the same effect. Moreover, you can fine-tune RWKV into a non-parallelizable RNN (then you can use outputs of later layers of the previous token) if you want extra performance.
+
+**UPDATE**: I am testing RWKV-4 fp16! 100% faster training than tf32. Scaling to 3B and 7B soon.
 
 ## Join our Discord: https://discord.gg/bDSBUMeFpc :)