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Copyright (C) 2024 Xiaomi Corporation.
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Licensed under the [Gemma](https://ai.google.dev/gemma/terms).
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GemmaX2-28 is a model based on the Gamma2 architecture, further pre-trained and fine-tuned with instruction-based objectives across 28 languages, and Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.
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# GemmaX2-28-2B-v0.1
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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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GemmaX2-28-2B-v0.1
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## Model Description
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GemmaX2-28-2B-v0.1 is an LLM-based translation model. It has been fintuned on GemmaX2-28-2B-Pretrain, which is a language model developed through continual pretraining of Gemma2-2B 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/pdf/2502.02481).
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- **Developed by:** Xiaomi
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- **Model type:** GemmaX2-28-2B-Pretrain is obtained by continually pretraining Gemma2-2B on a large amount of monolingual and parallel data. Subsequently, GemmaX2-28-2B-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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## 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 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 GemmaX2-28-2B, and future models will be released in due course.
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{
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"_name_or_path": "/mnt/pfs/cuimenglong/work/2024Q4/Gemma2-2B/Pretrain/checkpoints/Gemma2-2B/checkpoint-35096",
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2304,
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"initializer_range": 0.02,
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"intermediate_size": 9216,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 8,
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"num_hidden_layers": 26,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.45.0",
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"use_cache": true,
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"vocab_size": 256000
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}
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.45.0"，
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"max_new_tokens": 512
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}
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Reference in New Issue