---
license: gemma
base_model:
- ModelSpace/GemmaX2-28-9B-Pretrain
pipeline_tag: translation
library_name: transformers
---


## Model Description

GemmaX2-28-9B-v0.2 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).


- **Supported 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.
- **GitHub**: Please find more details in our [GitHub repository](https://github.com/xiaomi-research/gemmax).
- **Developed by**: Xiaomi Inc.


## Model Performance

**Update**: GemmaX2-28-9B-v0.2 adopts the translation instructions used for finetuning the [Xiaomi MiMT-46](https://huggingface.co/collections/xiaomi-research/xiaomi-mimt-46) models, in contrast to [GemmaX2-28-9B-v0.1](https://huggingface.co/ModelSpace/GemmaX2-28-9B-v0.1).


## Translation Prompt

```text
Translate this from <source language name> to <target language name>:
<source language name>: <source language sentence>
<target language name>:
```
Please use the language name specified above in the translation prompt.


## Run the model

#### Using on vLLM:
```python
from vllm import LLM, SamplingParams


model_id = "xiaomi-research/GemmaX2-28-9B-v0.2"

model = LLM(model=model_id)
sampling_params = SamplingParams(top_k=1, temperature=0, max_tokens=2048)

text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"

outputs = model.generate(text, sampling_params)
print(outputs[0].outputs[0].text)
```

#### Using on Transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "xiaomi-research/GemmaX2-28-9B-v0.2"

model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

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

outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```


## Citation

```bibtex
@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 currently supports only the 28 languages listed above, and strong translation performance is not guaranteed for other languages. We will continue to improve the translation quality of GemmaX2-28, and future model releases will follow in due course.