RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). 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.
What are the main differences and improvements between V1, V2, V3, V4, and V4-neo, and are there any related documents or explanations available?