tencent/Hunyuan-MT-7B
<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> | 🪡 <a href="https://github.com/Tencent/AngelSlim/tree/main"><b>AngelSlim</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
Hunyuan-MT-Chimera-7B-fp8 was produced by AngelSlim. 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
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:The multiple <target_language> translations:
- ``
<translated_text1>`` - ``
<translated_text2>`` - ``
<translated_text3>`` - ``
<translated_text4>`` - ``
<translated_text5>`` - ``
<translated_text6>``
### Use with transformers
First, please install transformers, recommends v4.56.0pip 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
from transformers import AutoModelForCausalLM, AutoTokenizer import os
modelnameor_path = "tencent/Hunyuan-MT-7B"
tokenizer = AutoTokenizer.frompretrained(modelnameorpath) model = AutoModelForCausalLM.frompretrained(modelnameorpath, devicemap="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."}, ] tokenizedchat = tokenizer.applychattemplate( messages, tokenize=True, addgenerationprompt=False, return_tensors="pt" )
outputs = model.generate(tokenizedchat.to(model.device), maxnewtokens=2048) outputtext = 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.
{ "topk": 20, "topp": 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:
@misc{hunyuanmt2025, title={Hunyuan-MT Technical Report}, 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}, howpublished={\url{https://github.com/Tencent-Hunyuan/Hunyuan-MT}}, year={2025} }