| --- |
| library_name: transformers |
| tags: |
| - translation |
| language: |
| - zh |
| - en |
| - fr |
| - pt |
| - es |
| - ja |
| - tr |
| - ru |
| - ar |
| - ko |
| - th |
| - it |
| - de |
| - vi |
| - ms |
| - id |
| - tl |
| - hi |
| - pl |
| - cs |
| - nl |
| - km |
| - my |
| - fa |
| - gu |
| - ur |
| - te |
| - mr |
| - he |
| - bn |
| - ta |
| - uk |
| - bo |
| - kk |
| - mn |
| - ug |
| --- |
| |
|
|
| <p align="center"> |
| <img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br> |
| </p><p></p> |
|
|
|
|
| <p align="center"> |
| 🤗 <a href="https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597"><b>Hugging Face</b></a> | |
| 🤖 <a href="https://modelscope.cn/collections/Hunyuan-MT-2ca6b8e1b4934f"><b>ModelScope</b></a> | |
| </p> |
| |
| <p align="center"> |
| 🖥️ <a href="https://hunyuan.tencent.com"><b>Official Website</b></a> | |
| 🕹️ <a href="https://hunyuan.tencent.com/modelSquare/home/list"><b>Demo</b></a> |
| </p> |
| |
| <p align="center"> |
| <a href="https://github.com/Tencent-Hunyuan/Hunyuan-MT"><b>GITHUB</b></a> |
| </p> |
| |
|
|
| ## Model Introduction |
|
|
| 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. |
|
|
| ### Key Features and Advantages |
|
|
| - In the WMT25 competition, the model achieved first place in 30 out of the 31 language categories it participated in. |
| - Hunyuan-MT-7B achieves industry-leading performance among models of comparable scale |
| - Hunyuan-MT-Chimera-7B is the industry’s first open-source translation ensemble model, elevating translation quality to a new level |
| - 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 |
|
|
| ## Related News |
| * 2025.9.1 We have open-sourced **Hunyuan-MT-7B** , **Hunyuan-MT-Chimera-7B** on Hugging Face. |
| <br> |
|
|
|
|
| |
|
|
| ## 模型链接 |
| | Model Name | Description | Download | |
| | ----------- | ----------- |----------- |
| | Hunyuan-MT-7B | Hunyuan 7B translation model |🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B)| |
| | Hunyuan-MT-7B-fp8 | Hunyuan 7B translation model，fp8 quant | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B-fp8)| |
| | Hunyuan-MT-Chimera | Hunyuan 7B translation ensemble model | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B)| |
| | Hunyuan-MT-Chimera-fp8 | Hunyuan 7B translation ensemble model，fp8 quant | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B-fp8)| |
|
|
| ## Prompts |
|
|
| ### Prompt Template for ZH<=>XX Translation. |
|
|
| ``` |
| |
| 把下面的文本翻译成<target_language>，不要额外解释。 |
| |
| <source_text> |
| |
| ``` |
|
|
| ### Prompt Template for XX<=>XX Translation, excluding ZH<=>XX. |
|
|
| ``` |
| |
| Translate the following segment into <target_language>, without additional explanation. |
| |
| <source_text> |
| |
| ``` |
|
|
| ### Prompt Template for Hunyuan-MT-Chmeria-7B |
|
|
| ``` |
| |
| 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. |
| |
| The <source_language> segment: |
| ```<source_text>``` |
| |
| The multiple <target_language> translations: |
| 1. ```<translated_text1>``` |
| 2. ```<translated_text2>``` |
| 3. ```<translated_text3>``` |
| 4. ```<translated_text4>``` |
| 5. ```<translated_text5>``` |
| 6. ```<translated_text6>``` |
| |
| ``` |
|
|
| |
|
|
| ### Use with transformers |
| First, please install transformers, recommends v4.56.0 |
| ```SHELL |
| pip install transformers==4.56.0 |
| ``` |
|
|
| The following code snippet shows how to use the transformers library to load and apply the model. |
|
|
| *!!! 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.* |
|
|
| we use tencent/Hunyuan-MT-7B for example |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import os |
| |
| model_name_or_path = "tencent/Hunyuan-MT-7B" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
| model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto") # You may want to use bfloat16 and/or move to GPU here |
| messages = [ |
| {"role": "user", "content": "Translate the following segment into Chinese, without additional explanation.\n\nIt’s on the house."}, |
| ] |
| tokenized_chat = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=False, |
| return_tensors="pt" |
| ) |
| |
| outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048) |
| output_text = tokenizer.decode(outputs[0]) |
| ``` |
|
|
| We recommend using the following set of parameters for inference. Note that our model does not have the default system_prompt. |
| |
| ```json |
| { |
| "top_k": 20, |
| "top_p": 0.6, |
| "repetition_penalty": 1.05, |
| "temperature": 0.7 |
| } |
| ``` |
| |
| Supported languages: |
| | Languages | Abbr. | Chinese Names | |
| |-------------------|---------|-----------------| |
| | Chinese | zh | 中文 | |
| | English | en | 英语 | |
| | French | fr | 法语 | |
| | Portuguese | pt | 葡萄牙语 | |
| | Spanish | es | 西班牙语 | |
| | Japanese | ja | 日语 | |
| | Turkish | tr | 土耳其语 | |
| | Russian | ru | 俄语 | |
| | Arabic | ar | 阿拉伯语 | |
| | Korean | ko | 韩语 | |
| | Thai | th | 泰语 | |
| | Italian | it | 意大利语 | |
| | German | de | 德语 | |
| | Vietnamese | vi | 越南语 | |
| | Malay | ms | 马来语 | |
| | Indonesian | id | 印尼语 | |
| | Filipino | tl | 菲律宾语 | |
| | Hindi | hi | 印地语 | |
| | Traditional Chinese | zh-Hant| 繁体中文 | |
| | Polish | pl | 波兰语 | |
| | Czech | cs | 捷克语 | |
| | Dutch | nl | 荷兰语 | |
| | Khmer | km | 高棉语 | |
| | Burmese | my | 缅甸语 | |
| | Persian | fa | 波斯语 | |
| | Gujarati | gu | 古吉拉特语 | |
| | Urdu | ur | 乌尔都语 | |
| | Telugu | te | 泰卢固语 | |
| | Marathi | mr | 马拉地语 | |
| | Hebrew | he | 希伯来语 | |
| | Bengali | bn | 孟加拉语 | |
| | Tamil | ta | 泰米尔语 | |
| | Ukrainian | uk | 乌克兰语 | |
| | Tibetan | bo | 藏语 | |
| | Kazakh | kk | 哈萨克语 | |
| | Mongolian | mn | 蒙古语 | |
| | Uyghur | ug | 维吾尔语 | |
| | Cantonese | yue | 粤语 | |
| |
| Citing Hunyuan-MT: |
| |
| ```bibtex |
| @misc{hunyuan_mt, |
| title={Hunyuan-MT Technical Report}, |
| author={Mao Zheng and Zheng Li and Bingxin Qu and Mingyang Song and Yang Du and Mingrui Sun and Di Wang}, |
| year={2025}, |
| eprint={2509.05209}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2509.05209}, |
| } |
| ``` |
