model-architecture-rwkv

Topics in the source

• RWKV - Receptance Weighted Key Value
• Quick start
• Common workflows
• Workflow 1: Text generation (streaming)
• Workflow 2: Long context processing (infinite context)
• Workflow 3: Fine-tuning RWKV
• Workflow 4: RWKV vs Transformer comparison
• When to use vs alternatives

RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference). Installation: Basic usage (GPT mode + RNN mode): Efficient token-by-token generation: Key advantage: Constant memory per token (no growing KV cache) Process million-token sequences: Standard fine-tuning workflow: Memory comparison (1M token sequence): Speed comparison (inference): Use RWKV when: Need very long context (100K+ tokens) Want constant memory usage Building streaming applications Need RNN efficiency with Transformer performance Memory-constrained deployment Key advantages: Linear time: O(n) vs O(n²) for Transformers No KV cache: Constant memory per token Infinite context: No fixed window limit Parallelizable training: Like GPT Sequential inference: Like RNN Use alternatives instead: Transformers: Need absolute best performance, have compute Mamba: Want state-space models RetNet: Need retention mechanism Hyena: Want convolution-based approach Issue: Out of memory during training Use gradient checkpointing and DeepSpeed: Issue: Slow inference Enable CUDA kernel: Issue: Model not loading Check model path and strategy: Issue: State management in RNN mode Always pass state between f

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