Instructions to use Hcompany/NeoMME-260M-Pretrain-predecay-s450000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hcompany/NeoMME-260M-Pretrain-predecay-s450000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Hcompany/NeoMME-260M-Pretrain-predecay-s450000")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskedLM processor = AutoProcessor.from_pretrained("Hcompany/NeoMME-260M-Pretrain-predecay-s450000") model = AutoModelForMaskedLM.from_pretrained("Hcompany/NeoMME-260M-Pretrain-predecay-s450000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: add arXiv citation url
Browse files
README.md
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[](https://huggingface.co/docs/transformers/en/model_doc/neomme)
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[](https://hf.co/collections/Hcompany/neomme)
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[](https://huggingface.co/docs/transformers/en/model_doc/neomme)
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[](https://hf.co/collections/Hcompany/neomme)
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[](https://arxiv.org/abs/2609.01657)
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## Model summary
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## Citation
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```bibtex
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@misc{lac2026neommesingletowermultimodalnativemultilingual,
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title={NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference},
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author={Aurélien Lac and Tony Wu},
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year={2026},
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eprint={2609.01657},
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archivePrefix={arXiv},
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primaryClass={cs.IR},
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url={https://arxiv.org/abs/2609.01657},
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}
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```
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