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Provides guidance and workflows for RWKV, a Transformer+RNN hybrid that enables linear-time inference and effectively unlimited context for long-sequence and st
Detailed instructions and workflows for using RWKV (Receptance Weighted Key Value), a hybrid model that trains like a Transformer but infers like an RNN, offering O(1) memory per token and effectively infinite context. Includes installation steps, example code for GPT and RNN modes, workflows for text generation, long-context processing, fine-tuning, and comparisons versus standard Transformers.
Use this skill when you need to: process very long documents or streaming data with constant memory, build memory-efficient inference pipelines, fine-tune RWKV models, or compare RWKV versus Transformer approaches for large-context tasks. Ideal for research, production inference optimization, and low-memory deployments.
Best used by code-capable agents (Claude Code, Copilot/Codex-style agents, or any agent that can run Python snippets and manage environment setup).
This skill has not been reviewed by our automated audit pipeline yet.
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