Loading
The Transformer Bottleneck The transformer architecture revolutionized natural language processing—but its greatest strength is now a core limitation. Self-attention enables models to consider all tokens simultaneously, but this also means…
Read more1. High-Level Philosophy RWKV solves a core limitation in traditional Transformers: quadratic attention cost with sequence length. Instead of computing full attention matrices, it introduces a mechanism to simulate attention…
Read moreOverview We are building a multi-agent AI cluster using RWKV-7 models. Your focus is on low-resource, efficient models that support long-term memory, recursion, and modular specialization. We’re using RWKV due…
Read more
