87 lines
3.1 KiB
Markdown
87 lines
3.1 KiB
Markdown
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---
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license: gemma
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base_model:
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- xiaomi-research/MiMT-46-1B-Pretrain
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pipeline_tag: translation
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library_name: transformers
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---
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## Model Description
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MiLMMT-46-1B-v0.1 is an LLM-based translation model. It has been fintuned on MiLMMT-46-1B-Pretrain, which is a language model developed through continual pretraining of Gemma3-1B 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](https://arxiv.org/abs/2602.11961).
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- **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).
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- **GitHub**: Please find more details in our [GitHub repository](https://github.com/xiaomi-research/gemmax).
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- **Developed by**: Xiaomi Inc.
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## Model Performance
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## Translation Prompt
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```text
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Translate this from <source language name> to <target language name>:
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<source language name>: <source language sentence>
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<target language name>:
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```
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Please use the language name specified above in the translation prompt.
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## Run the model
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#### Using on vLLM:
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```python
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from vllm import LLM, SamplingParams
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model_id = "xiaomi-research/MiLMMT-46-1B-v0.1"
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model = LLM(model=model_id)
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sampling_params = SamplingParams(top_k=1, temperature=0, max_tokens=2048)
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text = "Translate this from Chinese (Simplified) to English:\nChinese (Simplified): 我爱机器翻译\nEnglish:"
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outputs = model.generate(text, sampling_params)
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print(outputs[0].outputs[0].text)
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```
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#### Using on Transformers:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "xiaomi-research/MiLMMT-46-1B-v0.1"
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model = AutoModelForCausalLM.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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text = "Translate this from Chinese (Simplified) to English:\nChinese (Simplified): 我爱机器翻译\nEnglish:"
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inputs = tokenizer(text, add_special_tokens=False, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=1024)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Citation
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```bibtex
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@misc{shang2026scalingmodeldatamultilingual,
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title={Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models},
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author={Yuzhe Shang and Pengzhi Gao and Wei Liu and Jian Luan and Jinsong Su},
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year={2026},
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eprint={2602.11961},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2602.11961},
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}
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```
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## Limitations
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MiLMMT-46 currently supports only the 46 languages listed above, and strong translation performance is not guaranteed for other languages. We will continue to improve the translation quality of MiLMMT-46, and future model releases will follow in due course.
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