Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [[arXiv]](https://arxiv.org/abs/2506.04598) [[NeurIPS 2025]](https://openreview.net/forum?id=cWnZLIdeKn)
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updated May 5, 2026
README
Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [arXiv] [NeurIPS 2025]
We provide pre-trained models across various scales (model scales: S/32 - H/14; samples seen scales: 1.28M - 3.07B; datasets: Re-LAION 1.4B, DataComp 1.4B, DFN-1.4B), including all the intermediate checkpoints during the training, used in the paper Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [arXiv], [NeurIPS 2025] Please refer to the official Github repository for more information about how to reproduce the results download and use the models.
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Linked in READMEs
Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [[arXiv]](https://arxiv.org/abs/2506.04598) [[NeurIPS 2025]](https://openreview.net/forum?id=cWnZLIdeKn)
3
2 commits
1 linked in READMEs
updated May 5, 2026
README
Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [arXiv] [NeurIPS 2025]
We provide pre-trained models across various scales (model scales: S/32 - H/14; samples seen scales: 1.28M - 3.07B; datasets: Re-LAION 1.4B, DataComp 1.4B, DFN-1.4B), including all the intermediate checkpoints during the training, used in the paper Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets [arXiv], [NeurIPS 2025] Please refer to the official Github repository for more information about how to reproduce the results download and use the models.