The exponential growth of large language models poses significant challenges in terms of deployment costs and environmental impact due to high energy consumption. In response to these challenges, this paper introduces BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. By introducing BitLinear as a replacement for the traditional nn.Linear layer, BitNet aims to train with 1-bit weights from scratch, significantly reducing the memory footprint and energy consumption while maintaining competitive performance. …
BERT, known for its masked language modeling (MLM) approach, has been a cornerstone in pre-training models for NLP. XLNet built on this by introducing permuted language modeling (PLM) to capture the dependency among predicted tokens. However, XLNet fell short in utilizing the full position information within a sentence, leading to discrepancies between pre-training and fine-tuning phases. MPNet emerges as a novel solution that amalgamates the strengths of BERT and XLNet while overcoming their limitations. By leveraging permuted language modeling and incorporating auxiliary position information, MPNet provides a comprehensive view of the sentence structure. This method not only enhances the model's understanding of language but also aligns more closely with downstream tasks. Pre-trained on an extensive dataset exceeding 160GB and fine-tuned across various benchmarks like GLUE and SQuAD, MPNet demonstrates superior performance over existing models, including BERT, XLNet, and RoBERTa. For further details and access to the pre-trained models, visit Microsoft's MPNet repository. …
The landscape of deep learning is continually evolving, and a recent groundbreaking development comes from the world of sequence modeling. A paper titled "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" introduces a novel approach that challenges the current dominance of Transformer-based models. Let's delve into this innovation. …
The Annotated S4 website delves into the Structured State Space (S4) architecture, revolutionizing long-range sequence modeling in various domains, including vision, language, and audio. It distinctly moves away from Transformer models, handling over 16,000 sequence elements effectively. …
In the realm of machine learning, the Transformer model has been nothing short of revolutionary. Originating from the field of natural language processing, its ability to capture sequential relationships in data has set new benchmarks across various applications. However, its adaptation to the specific nuances of time series data has remained a complex challenge, until now. …
Transformers have revolutionized the field of deep learning, offering unparalleled performance in tasks like natural language processing and computer vision. However, their complexity often translates to significant computational demands. Recent advancements, including Shaped Attention, the removal of certain parameters, and parallel block architectures, propose innovative ways to simplify transformers without compromising their effectiveness. …