Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads

Vision Transformers (ViTs) have demonstrated remarkable potential in image processing tasks by utilizing self-attention mechanisms to capture global relationships within data. However, their scalability is hindered by significant computational and memory demands, especially for large-scale models with many parameters. This study aims to leverage DeepSpeed, a highly efficient distributed training framework that is commonly used for language models, to enhance the scalability and performance of ViTs. We evaluate intra- and inter-node training efficiency across multiple GPU configurations on various datasets like CIFAR-10 and CIFAR-100, exploring the impact of distributed data parallelism on training speed, communication overhead, and overall scalability (strong and weak scaling). By systematically varying software parameters, such as batch size and gradient accumulation, we identify key factors influencing performance of distributed training. The experiments in this study provide a foundational basis for applying DeepSpeed to image-related tasks. Future work will extend these investigations to deepen our understanding of DeepSpeed's limitations and explore strategies for optimizing distributed training pipelines for Vision Transformers.

Say It Another Way: A Framework for User-Grounded Paraphrasing

Small changes in how a prompt is worded can lead to meaningful differences in the behavior of large language models (LLMs), raising concerns about the stability and reliability of their evaluations. While prior work has explored simple formatting changes, these rarely capture the kinds of natural variation seen in real-world language use. We propose a controlled paraphrasing framework based on a taxonomy of minimal linguistic transformations to systematically generate natural prompt variations. Using the BBQ dataset, we validate our method with both human annotations and automated checks, then use it to study how LLMs respond to paraphrased prompts in stereotype evaluation tasks. Our analysis shows that even subtle prompt modifications can lead to substantial changes in model behavior. These results highlight the need for robust, paraphrase-aware evaluation protocols.