RWKV LM | Awesome Repository

RWKV-LM is a framework for training and deploying recurrent language models. It utilizes a linear-time recurrent architecture that enables text generation and sequence processing with constant memory and time complexity, avoiding the quadratic scaling of traditional attention caches.

The project implements a parallelizable training mechanism that allows recurrent models to be trained using global operations while maintaining cache-free inference. It includes state-tuning capabilities to optimize the initial hidden state and utilizes adaptive probability-mass sampling to control token diversity during generation.

The system covers the full lifecycle of large language model development, including recurrent model training, custom fine-tuning via datasets, and high-dimensional text embedding extraction.

Features

Linear-Time Sequence Models - Implements a linear-time recurrent architecture that processes sequences with constant memory and time complexity.
Generative Text Inference - Produces token sequences via a recurrent architecture that maintains infinite context length without a cache.
Large Language Models - Implements a recurrent architecture for processing long sequences and generating text without traditional cache overhead.
Recurrent Parallel Training - Ships a parallelizable training mechanism that combines transformer-like global operations with recurrent inference properties.

Features

Linear-Time Sequence Models - Implements a linear-time recurrent architecture that processes sequences with constant memory and time complexity.
Generative Text Inference - Produces token sequences via a recurrent architecture that maintains infinite context length without a cache.
Large Language Models - Implements a recurrent architecture for processing long sequences and generating text without traditional cache overhead.
Recurrent Parallel Training - Ships a parallelizable training mechanism that combines transformer-like global operations with recurrent inference properties.

The system covers the full lifecycle of large language model development, including recurrent model training, custom fine-tuning via datasets, and high-dimensional text embedding extraction.