AI & Machine Learning

RWKV Architecture

Covers the RWKV (Receptance Weighted Key Value) architecture, an RNN/Transformer hybrid with O(n) inference and no KV cache, including RWKV-7, its parallel GPT-mode training and sequential RNN-mode inference, state passing, fine-tuning with DeepSpeed, and CUDA kernel setup. Use when generating text token by token with constant memory, processing very long contexts of 100K+ tokens, fine-tuning an RWKV model, comparing RWKV memory and speed against Transformers, or debugging RWKV state handling, loading, or out-of-memory errors. Prefer a standard Transformer when peak accuracy matters more than memory, and Mamba for state-space models.

Publisher / source

KalarisLabs/research-agent-skills (found on the CSV list)

Upstream record

KalarisLabs/research-agent-skills

Version

Version 2

Published

2026-11-02

Publication review

Approved for publication

Content fingerprint

310340fbb8fc7e235536de9069bb351a842ca5643d8433fcb6102e8f3d4c6804