初始化项目，由ModelHub XC社区提供模型

Model: xiaomi-research/MiLMMT-46-1B-v0.1
Source: Original Platform

README.md

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+---
+license: gemma
+base_model:
+- xiaomi-research/MiMT-46-1B-Pretrain
+pipeline_tag: translation
+library_name: transformers
+---
+
+
+## Model Description
+
+MiLMMT-46-1B-v0.1 is an LLM-based translation model. It has been fintuned on MiLMMT-46-1B-Pretrain, which is a language model developed through continual pretraining of Gemma3-1B using a mix of 143 billion tokens from both monolingual and parallel data across 46 different languages. Please find more details in our paper: [Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models](https://arxiv.org/abs/2602.11961).
+
+
+- **Supported Languages**: Arabic, Azerbaijani, Bulgarian, Bengali, Catalan, Czech, Danish, German, Greek, English, Spanish, Persian, Finnish, French, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese, Kazakh, Khmer, Korean, Lao, Malay, Burmese, Norwegian, Dutch, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Swedish, Tamil, Thai, Tagalog, Turkish, Urdu, Uzbek, Vietnamese, Cantonese, Chinese (Simplified), Chinese (Traditional).
+- **GitHub**: Please find more details in our [GitHub repository](https://github.com/xiaomi-research/gemmax).
+- **Developed by**: Xiaomi Inc.
+
+
+## Model Performance
+
+![Experimental Result](main.png)
+
+## 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/MiLMMT-46-1B-v0.1"
+
+model = LLM(model=model_id)
+sampling_params = SamplingParams(top_k=1, temperature=0, max_tokens=2048)
+
+text = "Translate this from Chinese (Simplified) to English:\nChinese (Simplified): 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/MiLMMT-46-1B-v0.1"
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+text = "Translate this from Chinese (Simplified) to English:\nChinese (Simplified): 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{shang2026scalingmodeldatamultilingual,
+      title={Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models}, 
+      author={Yuzhe Shang and Pengzhi Gao and Wei Liu and Jian Luan and Jinsong Su},
+      year={2026},
+      eprint={2602.11961},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2602.11961}, 
+}
+```
+
+
+## Limitations
+
+MiLMMT-46 currently supports only the 46 languages listed above, and strong translation performance is not guaranteed for other languages. We will continue to improve the translation quality of MiLMMT-46, and future model releases will follow in due course.