---
library_name: transformers
tags:
- translation
---
🤗 Hugging Face |
🤖 ModelScope |
🖥️ Official Website |
🕹️ Demo
GITHUB
## 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.
## 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，不要额外解释。
```
### Prompt Template for XX<=>XX Translation, excluding ZH<=>XX.
```
Translate the following segment into , without additional explanation.
```
### Prompt Template for Hunyuan-MT-Chmeria-7B
```
Analyze the following multiple translations of the segment surrounded in triple backticks and generate a single refined translation. Only output the refined translation, do not explain.
The segment:
``````
The multiple translations:
1. ``````
2. ``````
3. ``````
4. ``````
5. ``````
6. ``````
```
### Use with transformers
First, please install transformers, recommends v4.56.0
```SHELL
pip install transformers==v4.56.0
```
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
}
```
Citing Hunyuan-MT:
```bibtex
@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}
}
```