Transformer Based Model

Transformer-based models are a class of neural networks achieving state-of-the-art results across diverse fields by leveraging self-attention mechanisms to capture long-range dependencies in sequential data. Current research focuses on addressing limitations such as quadratic computational complexity for long sequences, leading to the development of alternative architectures like Mamba and modifications such as LoRA for efficient adaptation and inference. These advancements are significantly impacting various applications, from speech recognition and natural language processing to computer vision and time-series forecasting, by improving both accuracy and efficiency on resource-constrained devices.