99 lines
2.9 KiB
Markdown
99 lines
2.9 KiB
Markdown
---
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license: gemma
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license_name: license
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license_link: LICENSE
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metrics:
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- bleu
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- comet
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base_model:
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- ModelSpace/GemmaX2-28-9B-Pretrain
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pipeline_tag: translation
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library_name: transformers
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language:
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- ar
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- bn
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- cs
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- de
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- en
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- es
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- fa
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- fr
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- he
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- hi
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- id
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- it
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- ja
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- km
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- ko
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- lo
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- ms
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- my
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- nl
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- pl
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- pt
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- ru
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- th
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- tl
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- tr
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- ur
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- vi
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- zh
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---
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## Updates
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New multilingual machine translation model (MiLMMT-46) is now available. Please check the [link](https://huggingface.co/collections/xiaomi-research/milmmt-46) for detailed information.
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## Model Description
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GemmaX2-28-9B-v0.1 is an LLM-based translation model. It has been fintuned on GemmaX2-28-9B-Pretrain, which is a language model developed through continual pretraining of Gemma2-9B using a mix of 56 billion tokens from both monolingual and parallel data across 28 different languages. Please find more details in our paper: [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://arxiv.org/abs/2502.02481).
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- **Developed by:** Xiaomi
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- **Model type:** GemmaX2-28-9B-Pretrain is obtained by continually pretraining Gemma2-9B on a large amount of monolingual and parallel data. Subsequently, GemmaX2-28-9B-v0.1 is derived through supervised finetuning on a small set of high-quality translation instruction data.
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- **Languages:** Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, Polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese.
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- **Github:** Please find more details in our [Github repository](https://github.com/xiaomi-research/gemmax).
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## Model Performance
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## Run the model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ModelSpace/GemmaX2-28-9B-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=512)
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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{cui2025multilingualmachinetranslationopen,
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title={Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study},
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author={Menglong Cui and Pengzhi Gao and Wei Liu and Jian Luan and Bin Wang},
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year={2025},
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eprint={2502.02481},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.02481},
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}
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```
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## Limitations
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GemmaX2-28-9B-v0.1 only supports the 28 languages listed above and does not guarantee strong translation performance for other languages. We will continue to enhance the translation performance of GemmaX2-28-9B, and future models will be released in due course.
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