Large Scale Transformer Model

Large-scale transformer models are revolutionizing various fields by achieving state-of-the-art performance in tasks like language modeling and image recognition. Current research focuses on improving training efficiency through techniques like model compression (e.g., pruning, quantization, tensor train representations), optimizing distributed training strategies (e.g., pipeline parallelism), and developing more efficient architectures (e.g., hybrid models combining transformers with RNNs). These advancements aim to reduce the substantial computational costs associated with training and deploying these powerful, yet resource-intensive, models, thereby broadening their accessibility and impact across diverse applications.