README.md

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    🤗&nbsp;<a href="https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
    <img src="https://avatars.githubusercontent.com/u/109945100?s=200&v=4" width="16"/>&nbsp;<a href="https://modelscope.cn/collections/Hunyuan-MT-2ca6b8e1b4934f"><b>ModelScope</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
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    🖥️&nbsp;<a href="https://hunyuan.tencent.com" style="color: red;"><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;
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    <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.
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## 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>```


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### Use with transformers
First, please install transformers, recommends v4.55.4
```SHELL
pip install transformers==4.55.4
```

The following code snippet shows how to use the transformers library to load and apply the model.

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
}
```