ch-min/NVILA-Lite-2B-DATA_SCALE_EXP_800K-20251108_180221

NVILA-Lite-2B — Data-Scale Fine-tune (800k)

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 800k.

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`

Loading

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 using the transformers library.

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},
}