metadata

license: other
license_name: license
license_link: LICENSE
metrics:
  - bleu
base_model:
  - google/gemma-2-9b
pipeline_tag: translation
library_name: transformers

Model Card for GemmaX2-28

Model Details

Model Description

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.

Developed by: Xiaomi
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.
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.
License: gemma

Model Source

paper: Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study

Model Performance

Run the model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ModelSpace/GemmaX2-28-9B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

@misc{cui2025multilingualmachinetranslationopen,
      title={Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study}, 
      author={Menglong Cui and Pengzhi Gao and Wei Liu and Jian Luan and Bin Wang},
      year={2025},
      eprint={2502.02481},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.02481}, 
}

Limitations

GemmaX2-28-9B-v0.1 supports only the 28 most commonly used languages and does not guarantee powerful translation performance for other languages. Additionally, we will continue to improve GemmaX2-28-9B's translation performance, and future models will be release in due course.