ch-min/NVILA-Lite-2B-DATA_SCALE_EXP_400K-20251108_180221

NVILA-Lite-2B — Data-Scale Fine-tune (400k)

Fine-tune of `Efficient-Large-Model/NVILA-Lite-2B` on a 5-dataset spatial-reasoning mixture (SAT + RoboSpatial + SPAR-7M + RefSpatial + PRISM), with the training-sample budget set to 400k.

This checkpoint is one of the data-scale variants released alongside the paper "Why Far Looks Up: Probing Spatial Representation in Vision-Language Models" (see HF Collection).

• —📄 Paper: arXiv:2605.30161
• —🌐 Project page: https://cheolhong0916.github.io/whyfarlooksup.github.io/
• —💻 Probing / evaluation code: https://github.com/cheolhong0916/contrastive-probing
• —📊 Diagnostic benchmark: `cubec/spatialtunnel`

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This checkpoint inherits the architecture of `Efficient-Large-Model/NVILA-Lite-2B`. Load it with the same interface you would use for the base model.

Citation

If you use this checkpoint, please cite both our paper and the original NVILA-Lite-2B paper.

Our paper (this checkpoint family):

@article{min2026whyfarlooksup,
  title   = {Why Far Looks Up: Probing Spatial Representation in Vision-Language Models},
  author  = {Min, Cheolhong and Jung, Jaeyun and Lee, Daeun and Jeon, Hyeonseong and
             Su, Yu and Tremblay, Jonathan and Song, Chan Hee and Park, Jaesik},
  journal = {arXiv preprint arXiv:2605.30161},
  year    = {2026},
}

Original NVILA-Lite-2B ([NVILA: Efficient Frontier Visual Language Models](https://arxiv.org/abs/2412.04468)):

@article{liu2024nvila,
  title   = {NVILA: Efficient Frontier Visual Language Models},
  author  = {Liu, Zhijian and Zhu, Ligeng and Shi, Baifeng and others},
  journal = {arXiv preprint arXiv:2412.04468},
  year    = {2024},
}