This is a custom architecture for the nanoRWKV project from RWKV-v4neo. The architecture is based on the original nanoRWKV architecture, but with some modifications. This is RWKV «x051a» which does not require custom CUDA kernel to train, so it works for any GPU / CPU. > The nanoGPT-style implementation of RWKV Language Model — an RNN with GPT-level LLM performance. RWKV is essentially an RNN with unrivaled advantage when doing inference. Here we benchmark the speed and space occupation of RWKV, along with its Transformer counterpart (code could be found here). We could easily find: — single token generation latency of RWKV is an constant. — overall latency of RWKV is linear with respect to context length. — overall memory occupation of RWKV is an constant. Before kicking off this project, make sure you are familiar with the following concepts: — RNN: RNN stands for Recurrent Neural Network. It is a type of artificial neural network designed to work with sequential data or time-series data. Check this tutorial about RNN. — Transformer: A Transformer is a type of deep learning model introduced in the paper Attention is All You Need. It is specifically designed for handling…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: AnshulRanjan2004
Теги: LLM, RWKV, en
Лайков: 3 | Загрузок: 0
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
