Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate

Optimization story: Bloom inference

Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator

Making thousands of open LLMs bloom in the Vertex AI Model Garden

Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

arXiv:2606. 29975v1 Announce Type: new Abstract: Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts.

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

arXiv:2506. 01883v3 Announce Type: replace-cross Abstract: Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory.

Fast BIB simulation at a future Muon Collider with generative machine learning

The paper presents the first machine learning models for fast generation of beam‑induced background (BIB) in tracking detectors at a future Muon Collider. Two architectures are explored: a high‑fidelity tabular diffusion model and a faster circular spline flow model. Both produce BIB hits and tracks that closely match full simulation results, achieving over an order of magnitude speed‑up while requiring far less computational resources.