MegaScale-Data is a distributed data loading system that disaggregates preprocessing and applies auto-partitioning to deliver 4.5x higher end-to-end training throughput and 13.5x lower CPU memory usage for multisource large foundation models.
Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
2 Pith papers cite this work. Polarity classification is still indexing.
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ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training
MegaScale-Data is a distributed data loading system that disaggregates preprocessing and applies auto-partitioning to deliver 4.5x higher end-to-end training throughput and 13.5x lower CPU memory usage for multisource large foundation models.
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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.