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--- |
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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/GemmaX2-28-2B-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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# Model Card for GemmaX2-28 |
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## Model Details |
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### Model Description |
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GemmaX2-28-2B-Pretrain is a language model that results from continual pretraining of Gemma2-2B on a mix of 56 billion tokens of monolingual and parallel data in 28 different 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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GemmaX2-28-2B-v0.1 is the model version of GemmaX2-28-2B-Pretrain after SFT. |
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- **Developed by:** Xiaomi |
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- **Model type:** A 2B parameter model base on Gemma2, we obtained GemmaX2-28-2B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data. Afterward, GemmaX2-28-2B-v0.1 was derived through supervised fine-tuning on a small set of high-quality instruction data. |
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- **Language(s):** 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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- **License:** gemma |
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### Model Source |
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- paper: [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://arxiv.org/pdf/2502.02481) |
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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/GemmaX2-28-2B-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=50) |
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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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GemmaX2-28-2B-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 GemmaX2-28-2B's translation performance, and future models will be release in due course. |
