3 papers
cs.DC2025
Speeding up Model Loading with fastsafetensors
Takeshi Yoshimura, Tatsuhiro Chiba, Manish Sethi +2
The rapid increases in model parameter sizes introduces new challenges in pre-trained model loading. Currently, machine learning code often deserializes each parameter as a tensor…
cs.LG2025
Lossless Compression for LLM Tensor Incremental Snapshots
Daniel Waddington, Cornel Constantinescu
During the training of Large Language Models (LLMs), tensor data is periodically "checkpointed" to persistent storage to allow recovery of work done in the event of failure. The vo…
cs.LG2022
Non-Volatile Memory Accelerated Posterior Estimation
Andrew Wood, Moshik Hershcovitch, Daniel Waddington +2
Bayesian inference allows machine learning models to express uncertainty. Current machine learning models use only a single learnable parameter combination when making predictions,…