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rwkv-architecture

by davila7

RNN+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.

Installation

Pick a client and clone the repository into its skills directory.

Installation

Quick info

Author
davila7
Category
Security
GitHub repo

About this skill

RNN+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.

How to use

  1. Zainstaluj PyTorch z obsługą CUDA: pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121

  2. Zainstaluj zależności: pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade

  3. Zainstaluj bibliotekę RWKV: pip install rwkv

  4. Pobierz wstępnie wytrenowany model (np. RWKV-4-Pile-14B) i ustaw ścieżkę w kodzie.

  5. Załaduj model w Pythonie, ustawiając zmienne środowiskowe RWKV_JIT_ON=1 i RWKV_CUDA_ON=1 dla przyspieszenia, następnie inicjalizuj RWKV z wybraną strategią (np. 'cuda fp16').

  6. Użyj modelu w trybie GPT (przetwarzanie równoległe wielu tokenów) lub RNN (sekwencyjne przetwarzanie token po tokenie) — obie metody dają identyczne wyniki logitów, ale RNN jest bardziej efektywny dla generowania tekstu w czasie rzeczywistym.

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