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"> 🤗&nbsp;<a href="https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp; 🤖&nbsp;<a href="https://modelscope.cn/collections/Hunyuan-MT-2ca6b8e1b4934f"><b>ModelScope</b></a>&nbsp;&nbsp;|&nbsp;&nbsp; 🪡&nbsp;<a href="https://github.com/Tencent/AngelSlim/tree/main"><b>AngelSlim</b></a> </p> <p align="center"> 🖥️&nbsp;<a href="https://hunyuan.tencent.com"><b>Official Website</b></a>&nbsp;&nbsp;|&nbsp;&nbsp; 🕹️&nbsp;<a href="https://hunyuan.tencent.com/modelSquare/home/list"><b>Demo</b></a>&nbsp;&nbsp;&nbsp;&nbsp; </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:
```<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

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

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.

{
  "top_k": 20,
  "top_p": 0.6,
  "repetition_penalty": 1.05,
  "temperature": 0.7
}

Supported languages:

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}
}