ModernVBERT

weights

open(ungated MIT checkpoints on the ModernVBERT org - colmodernvbert, bimodernvbert, modernvbert-embed and the base model)

data

open(the paper states models, code and data are released, and the org publishes the NatCap modality-alignment corpus alongside the public document-retrieval sets it trains on)

code

open(the card releases the training codebase under MIT, and the paper releases intermediate checkpoints with it)

checkpoints

open(ModernVBERT/ablation_checkpoints, the intermediate and ablation runs behind the paper)

license

MIT(OSI)

MIT covers the architecture, weights and training codebase, the data is published in the same org, and the intermediate checkpoints are kept rather than discarded. The training corpus is a mixture of public sets plus the released NatCap corpus rather than one named dataset, but nothing here is withheld.

• https://arxiv.org/abs/2510.01149 recorded 2026-09-11

  'Models, code and data are available at this https URL.'

• https://huggingface.co/api/datasets?author=ModernVBERT&limit=50 recorded 2026-09-11

  the org publishes ModernVBERT/natcap and ModernVBERT/toy-colpali-train-set as public datasets.

• https://huggingface.co/api/models?author=ModernVBERT&limit=50 recorded 2026-09-11

  seven public repos including ModernVBERT/ablation_checkpoints; `gated: false` throughout.

• https://huggingface.co/ModernVBERT/colmodernvbert/raw/main/README.md recorded 2026-09-11

  `license: mit` front matter and 'We release the ModernVBERT model architectures, model weights, and training codebase under the MIT license.'; lists ColModernVBERT, BiModernVBERT, ModernVBERT-embed and the base model.

Adoption is measured as Hugging Face downloads for ColModernVBERT, which accounts for effectively the whole family since the bi-encoder, base and ablation repos draw only single digits.

• https://huggingface.co/api/models/ModernVBERT/colmodernvbert recorded 2026-09-11

  `downloads` (trailing 30 days) = 132434; `gated: false`; `private: false`.

ModernVBERT's published numbers are credible and it leads every other sub-1B model on the same table, but the read here is about narrow scope, not dated evidence: it is English-only at 250M parameters and trails ColPali by roughly a tier on the same benchmark.

• https://arxiv.org/abs/2510.01149 recorded 2026-09-11

  'a compact 250M-parameter vision-language encoder that outperforms recent models up to 10 times larger when fine-tuned on document retrieval tasks'.

• https://arxiv.org/html/2510.01149v1 recorded 2026-09-11

  Table 3, ViDoRe Leaderboard, nDCG@5: ColModernVBERT (ours) 0.25B 81.2 / 56.0 / 68.6 at .032s CPU query latency; ColPali 2.92B 81.6 / 56.8 / 69.2; ColQwen2.5 3.75B 89.5 / 61.5 / 75.5; ColFlor 0.17B 68.8 / 43.0 / 55.9.