RWKV
The new generation of big model architecture surpasses Transformer.
- Excellent performance: RWKV combines the advantages of RNN and Transformer, providing efficient inference and training performance, allowing users to quickly achieve excellent results in various tasks.
- Fast Training: The architecture design of RWKV allows users to train models at a faster speed, greatly reducing the development cycle of models and improving the efficiency of research and application.
- Save VRAM: Due to its unique design, RWKV can operate with lower video memory usage, making it suitable for resource limited development environments.
- Infinite context length: RWKV supports' infinite 'context length and can handle long text input, greatly enhancing the ability of natural language processing tasks.
- Free sentence embedding: RWKV provides free sentence embedding function, which facilitates text analysis and similarity calculation for developers and researchers, reducing the threshold for use.
- Multi language support: RWKV can support multiple languages and encodings, ensuring that global users can use it without barriers and promoting the popularization of technology.
- Active Open Source Community: RWKV has strong support from the open source community, allowing developers to easily conduct research, development, and application, forming a good ecosystem.
Product Details
RWKV is an innovative deep learning architecture that combines the best features of RNN and Transformer. It provides excellent performance, fast inference and training, and does not rely on self attention mechanisms, saving VRM and supporting 'infinite' context length. RWKV has performed well in multiple languages and encodings, becoming a popular choice for developers worldwide and driving the advancement of open-source big language models.
