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>```

pip install transformers==4.56.0

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])

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

@misc{hunyuan_mt,
      title={Hunyuan-MT Technical Report}, 
      author={Mao Zheng and Zheng Li and Bingxin Qu and Mingyang Song and Yang Du and Mingrui Sun and Di Wang},
      year={2025},
      eprint={2509.05209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.05209}, 
}