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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.

• 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

• 2025.9.1 We have open-sourced Hunyuan-MT-7B , Hunyuan-MT-Chimera-7B on Hugging Face.
  

 

Model Name	Description	Download
Hunyuan-MT-7B	Hunyuan 7B translation model	🤗 Model
Hunyuan-MT-7B-fp8	Hunyuan 7B translation model，fp8 quant	🤗 Model
Hunyuan-MT-Chimera	Hunyuan 7B translation ensemble model	🤗 Model
Hunyuan-MT-Chimera-fp8	Hunyuan 7B translation ensemble model，fp8 quant	🤗 Model

把下面的文本翻译成<target_language>，不要额外解释。

<source_text>

Translate the following segment into <target_language>, without additional explanation.

<source_text>

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>```

 

First, please install transformers, recommends v4.56.0

pip install transformers==v4.56.0

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

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
}