👉 Fluid models are advanced neural network architectures designed to handle sequential data, like text or time series, with a unique approach that blends elements of recurrent neural networks (RNNs) and transformers. Unlike traditional RNNs, which process data sequentially and can struggle with long-term dependencies due to vanishing gradients, fluid models maintain a consistent memory of the entire input sequence through a mechanism called "flow-based" or "dynamic memory." This allows them to efficiently capture long-range dependencies and adapt their memory capacity dynamically based on the input's complexity. By using a combination of convolutional layers for local context and transformers for global context, fluid models achieve superior performance in tasks such as language modeling, translation, and text generation, while being more computationally efficient than their predecessors.
