Instructions to use xiaomi-research/MiLMMT-46-12B-Pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaomi-research/MiLMMT-46-12B-Pretrain with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaomi-research/MiLMMT-46-12B-Pretrain", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Description
MiLMMT-46-12B-Pretrain is a language model developed through continual pretraining of Gemma3-12B using a mix of 143 billion tokens from both monolingual and parallel data across 46 different languages. Please find more details in our paper: Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models.
- Supported Languages: Arabic, Azerbaijani, Bulgarian, Bengali, Catalan, Czech, Danish, German, Greek, English, Spanish, Persian, Finnish, French, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese, Kazakh, Khmer, Korean, Lao, Malay, Burmese, Norwegian, Dutch, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Swedish, Tamil, Thai, Tagalog, Turkish, Urdu, Uzbek, Vietnamese, Cantonese, Chinese (Simplified), Chinese (Traditional).
- GitHub: Please find more details in our GitHub repository.
- Developed by: Xiaomi Inc.
Note that MiLMMT-46-12B-Pretrain is NOT a translation model.
Training Data
We collect monolingual data from DCAD-2000. For parallel data, we collect all Chinese-centric and English-centric parallel datasets from the OPUS collection up to August 2025 and conduct a series of filtering processes, such as language identification, semantic duplication filtering, quality filtering, and more.
Citation
@misc{shang2026scalingmodeldatamultilingual,
title={Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models},
author={Yuzhe Shang and Pengzhi Gao and Wei Liu and Jian Luan and Jinsong Su},
year={2026},
eprint={2602.11961},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.11961},
}
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