Rwkv Architecture
Skill Verified ActiveRNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
To provide developers with a deep understanding and practical guidance on using the RWKV model architecture, enabling them to leverage its efficient inference and linear complexity for long-context AI applications.
Features
- Hybrid RNN+Transformer architecture
- O(n) inference and linear time complexity
- Infinite context window with constant memory usage
- Parallelizable training like GPT, sequential inference like RNN
- Detailed installation, usage, and workflow examples
Use Cases
- Building AI applications requiring long-context processing
- Deploying models in memory-constrained environments
- Developing streaming AI services
- Fine-tuning RWKV models for specific tasks
Non-Goals
- Replacing Transformers for absolute best performance in compute-rich environments
- Focusing on state-space models (Mamba) or other specific architectures (RetNet, Hyena)
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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