Update README.md

README.md

@@ -34,9 +34,12 @@ How it works: RWKV gathers information to a number of channels, which are also d
 
 Here are some of my TODOs. Let's work together :)
 
-* HuggingFace integration, and optimized CPU & iOS & Android & WASM & WebGL inference. RWKV is a RNN and very friendly for edge devices. Let's make it possible to run a LLM on your phone.
+* HuggingFace integration (check https://github.com/huggingface/transformers/issues/17230
+), and optimized CPU & iOS & Android & WASM & WebGL inference. RWKV is a RNN and very friendly for edge devices. Let's make it possible to run a LLM on your phone. 
 
-* Test it on bidirectional & MLM tasks, and image & audio & video tokens.
+* Test it on bidirectional & MLM tasks, and image & audio & video tokens. I think RWKV can support Encoder-Decoder via this: for each decoder token, use a learned mixture of [decoder previous hidden state] & [encoder final hidden state].
+
+* Now training RWKV-4a with one single tiny extra attention (just a few extra lines comparing with RWKV-4) to further improve some difficult zeroshot tasks (such as LAMBADA) for smaller models. See https://github.com/BlinkDL/RWKV-LM/commit/a268cd2e40351ee31c30c5f8a5d1266d35b41829
 
 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.*