rwkv-architecture

To achieve constant memory usage for long context inference, use the RWKV architecture, which maintains an RNN state for O(n) inference time and constant memory per token, avoiding Transformer's O(n^2) complexity.

RWKV supports Transformer-level training parallelism by combining Transformer-like parallelization during training with RNN efficiency during inference, solving the high inference costs of traditional Transformer models.

To load an RWKV model in PyTorch, use the rwkv-architecture skill to load the RWKV-4-Pile-1B5 model and perform a forward pass with the provided token sequence using the torch and transformers dependencies.

You can process infinite context lengths without memory blow-up because RWKV processes sequences of virtually unlimited length without a fixed context window, enabling coherent conversations over extended periods.

RWKV distinguishes itself from traditional Transformers by achieving linear complexity and constant memory usage per token, solving quadratic complexity and memory issues for long sequences while maintaining training parallelism.

You need DeepSpeed and PyTorch Lightning to train RWKV models, as these dependencies provide the necessary framework for Transformer-level parallelization and efficient model training alongside torch and wandb.