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--- |
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library_name: transformers |
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tags: |
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- translation |
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--- |
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<p align="center"> |
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<img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br> |
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</p><p></p> |
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<p align="center"> |
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🤗 <a href="https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597"><b>Hugging Face</b></a> | |
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🤖 <a href="https://modelscope.cn/collections/Hunyuan-MT-2ca6b8e1b4934f"><b>ModelScope</b></a> | |
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</p> |
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<p align="center"> |
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🖥️ <a href="https://hunyuan.tencent.com"><b>Official Website</b></a> | |
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🕹️ <a href="https://hunyuan.tencent.com/modelSquare/home/list"><b>Demo</b></a> |
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</p> |
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<p align="center"> |
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<a href="https://github.com/Tencent-Hunyuan/Hunyuan-MT"><b>GITHUB</b></a> |
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</p> |
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## Model Introduction |
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The Hunyuan Translation Model comprises a translation model, Hunyuan-MT-7B, and an ensemble model, Hunyuan-MT-Chimera. The translation model is used to translate source text into the target language, while the ensemble model integrates multiple translation outputs to produce a higher-quality result. It primarily supports mutual translation among 33 languages, including five ethnic minority languages in China. |
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### Key Features and Advantages |
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- In the WMT25 competition, the model achieved first place in 30 out of the 31 language categories it participated in. |
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- Hunyuan-MT-7B achieves industry-leading performance among models of comparable scale |
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- Hunyuan-MT-Chimera-7B is the industry’s first open-source translation ensemble model, elevating translation quality to a new level |
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- A comprehensive training framework for translation models has been proposed, spanning from pretrain → cross-lingual pretraining (CPT) → supervised fine-tuning (SFT) → translation enhancement → ensemble refinement, achieving state-of-the-art (SOTA) results for models of similar size |
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## Related News |
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* 2025.9.1 We have open-sourced **Hunyuan-MT-7B** , **Hunyuan-MT-Chimera-7B** on Hugging Face. |
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<br> |
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## 模型链接 |
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| Model Name | Description | Download | |
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| ----------- | ----------- |----------- |
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| Hunyuan-MT-7B | Hunyuan 7B translation model |🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B)| |
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| Hunyuan-MT-7B-fp8 | Hunyuan 7B translation model，fp8 quant | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B-fp8)| |
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| Hunyuan-MT-Chimera | Hunyuan 7B translation ensemble model | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B)| |
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| Hunyuan-MT-Chimera-fp8 | Hunyuan 7B translation ensemble model，fp8 quant | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B-fp8)| |
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## Prompts |
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### Prompt Template for ZH<=>XX Translation. |
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``` |
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把下面的文本翻译成<target_language>，不要额外解释。 |
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<source_text> |
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``` |
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### Prompt Template for XX<=>XX Translation, excluding ZH<=>XX. |
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``` |
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Translate the following segment into <target_language>, without additional explanation. |
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<source_text> |
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``` |
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### Prompt Template for Hunyuan-MT-Chmeria-7B |
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``` |
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Analyze the following multiple <target_language> translations of the <source_language> segment surrounded in triple backticks and generate a single refined <target_language> translation. Only output the refined translation, do not explain. |
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The <source_language> segment: |
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```<source_text>``` |
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The multiple <target_language> translations: |
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1. ```<translated_text1>``` |
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2. ```<translated_text2>``` |
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3. ```<translated_text3>``` |
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4. ```<translated_text4>``` |
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5. ```<translated_text5>``` |
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6. ```<translated_text6>``` |
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``` |
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### Use with transformers |
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First, please install transformers, recommends v4.56.0 |
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```SHELL |
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pip install transformers==v4.56.0 |
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``` |
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The following code snippet shows how to use the transformers library to load and apply the model. |
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*!!! If you want to load fp8 model with transformers, you need to change the name"ignored_layers" in config.json to "ignore" and upgrade the compressed-tensors to compressed-tensors-0.11.0.* |
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we use tencent/Hunyuan-MT-7B for example |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import os |
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model_name_or_path = "tencent/Hunyuan-MT-7B" |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto") # You may want to use bfloat16 and/or move to GPU here |
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messages = [ |
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{"role": "user", "content": "Translate the following segment into Chinese, without additional explanation.\n\nIt’s on the house."}, |
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] |
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tokenized_chat = tokenizer.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=False, |
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return_tensors="pt" |
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) |
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outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048) |
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output_text = tokenizer.decode(outputs[0]) |
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``` |
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We recommend using the following set of parameters for inference. Note that our model does not have the default system_prompt. |
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```json |
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{ |
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"top_k": 20, |
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"top_p": 0.6, |
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"repetition_penalty": 1.05, |
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"temperature": 0.7 |
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} |
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``` |
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Supported languages: |
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| Languages | Abbr. | Chinese Names | |
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|-------------------|---------|-----------------| |
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| Chinese | zh | 中文 | |
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| English | en | 英语 | |
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| French | fr | 法语 | |
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| Portuguese | pt | 葡萄牙语 | |
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| Spanish | es | 西班牙语 | |
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| Japanese | ja | 日语 | |
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| Turkish | tr | 土耳其语 | |
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| Russian | ru | 俄语 | |
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| Arabic | ar | 阿拉伯语 | |
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| Korean | ko | 韩语 | |
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| Thai | th | 泰语 | |
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| Italian | it | 意大利语 | |
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| German | de | 德语 | |
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| Vietnamese | vi | 越南语 | |
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| Malay | ms | 马来语 | |
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| Indonesian | id | 印尼语 | |
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| Filipino | tl | 菲律宾语 | |
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| Hindi | hi | 印地语 | |
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| Traditional Chinese | zh-Hant| 繁体中文 | |
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| Polish | pl | 波兰语 | |
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| Czech | cs | 捷克语 | |
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| Dutch | nl | 荷兰语 | |
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| Khmer | km | 高棉语 | |
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| Burmese | my | 缅甸语 | |
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| Persian | fa | 波斯语 | |
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| Gujarati | gu | 古吉拉特语 | |
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| Urdu | ur | 乌尔都语 | |
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| Telugu | te | 泰卢固语 | |
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| Marathi | mr | 马拉地语 | |
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| Hebrew | he | 希伯来语 | |
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| Bengali | bn | 孟加拉语 | |
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| Tamil | ta | 泰米尔语 | |
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| Ukrainian | uk | 乌克兰语 | |
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| Tibetan | bo | 藏语 | |
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| Kazakh | kk | 哈萨克语 | |
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| Mongolian | mn | 蒙古语 | |
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| Uyghur | ug | 维吾尔语 | |
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| Cantonese | yue | 粤语 | |
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Citing Hunyuan-MT: |
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```bibtex |
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@misc{hunyuanmt2025, |
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title={Hunyuan-MT Technical Report}, |
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author={Mao Zheng, Zheng Li, Bingxin Qu, Mingyang Song, Yang Du, Mingrui Sun, Di Wang, Tao Chen, Jiaqi Zhu, Xingwu Sun, Yufei Wang, Can Xu, Chen Li, Kai Wang, Decheng Wu}, |
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howpublished={\url{https://github.com/Tencent-Hunyuan/Hunyuan-MT}}, |
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year={2025} |
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} |
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``` |
