tencent/HY-MT1.5-1.8B

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Model Introduction

Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality. After quantization, the 1.8B model can be deployed on edge devices and support real-time translation scenarios, making it widely applicable.

Key Features and Advantages

• HY-MT1.5-1.8B achieves the industry-leading performance among models of the same size, surpassing most commercial translation APIs.
• HY-MT1.5-1.8B supports deployment on edge devices and real-time translation scenarios, offering broad applicability.
• HY-MT1.5-7B, compared to its September open-source version, has been optimized for annotated and mixed-language scenarios.
• Both models support terminology intervention, contextual translation, and formatted translation.

Related News

• 2025.12.30, we have open-sourced HY-MT1.5-1.8B and HY-MT1.5-7B on Hugging Face.
• 2025.9.1, we have open-sourced Hunyuan-MT-7B , Hunyuan-MT-Chimera-7B on Hugging Face.

Performance

You can refer to our technical report for more experimental results and analysis.

Technical Report

Model Links

| Model Name | Description | Download | | ----------- | ----------- |----------- | HY-MT1.5-1.8B | Hunyuan 1.8B translation model |🤗 Model| | HY-MT1.5-1.8B-FP8 | Hunyuan 1.8B translation model, fp8 quant | 🤗 Model| | HY-MT1.5-1.8B-GPTQ-Int4 | Hunyuan 1.8B translation model, int4 quant | 🤗 Model| | HY-MT1.5-7B | Hunyuan 7B translation model | 🤗 Model| | HY-MT1.5-7B-FP8 | Hunyuan 7B translation model, fp8 quant | 🤗 Model| | HY-MT1.5-7B-GPTQ-Int4 | Hunyuan 7B translation model, int4 quant | 🤗 Model|

Prompts

Prompt Template for ZHXX Translation.

--- ``` 将以下文本翻译为{target_language}，注意只需要输出翻译后的结果，不要额外解释：

{source_text} ``` ---

Prompt Template for XXXX Translation, excluding ZHXX.

--- ``` Translate the following segment into {target_language}, without additional explanation.

{source_text} ``` ---

Prompt Template for terminology intervention.

--- ``` 参考下面的翻译： {source_term} 翻译成 {target_term}

将以下文本翻译为{target_language}，注意只需要输出翻译后的结果，不要额外解释： {source_text} ``` ---

Prompt Template for contextual translation.

--- ``` {context} 参考上面的信息，把下面的文本翻译成{target_language}，注意不需要翻译上文，也不要额外解释： {source_text}

``` ---

Prompt Template for formatted translation.

--- ``` 将以下之间的文本翻译为中文，注意只需要输出翻译后的结果，不要额外解释，原文中的标签表示标签内文本包含格式信息，需要在译文中相应的位置尽量保留该标签。输出格式为：str

{src_text_with_format} ``` ---

Use with transformers

First, please install transformers, recommends v4.56.0 ``SHELL pip install transformers==4.56.0 ``

!!! 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.

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

we use tencent/HY-MT1.5-1.8B for example

```python from transformers import AutoModelForCausalLM, AutoTokenizer import os

model_name_or_path = "tencent/HY-MT1.5-1.8B"

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 } ``

Supported languages: | Languages | Abbr. | Chinese Names | |-------------------|---------|-----------------| | Chinese | zh | 中文 | | English | en | 英语 | | French | fr | 法语 | | Portuguese | pt | 葡萄牙语 | | Spanish | es | 西班牙语 | | Japanese | ja | 日语 | | Turkish | tr | 土耳其语 | | Russian | ru | 俄语 | | Arabic | ar | 阿拉伯语 | | Korean | ko | 韩语 | | Thai | th | 泰语 | | Italian | it | 意大利语 | | German | de | 德语 | | Vietnamese | vi | 越南语 | | Malay | ms | 马来语 | | Indonesian | id | 印尼语 | | Filipino | tl | 菲律宾语 | | Hindi | hi | 印地语 | | Traditional Chinese | zh-Hant| 繁体中文 | | Polish | pl | 波兰语 | | Czech | cs | 捷克语 | | Dutch | nl | 荷兰语 | | Khmer | km | 高棉语 | | Burmese | my | 缅甸语 | | Persian | fa | 波斯语 | | Gujarati | gu | 古吉拉特语 | | Urdu | ur | 乌尔都语 | | Telugu | te | 泰卢固语 | | Marathi | mr | 马拉地语 | | Hebrew | he | 希伯来语 | | Bengali | bn | 孟加拉语 | | Tamil | ta | 泰米尔语 | | Ukrainian | uk | 乌克兰语 | | Tibetan | bo | 藏语 | | Kazakh | kk | 哈萨克语 | | Mongolian | mn | 蒙古语 | | Uyghur | ug | 维吾尔语 | | Cantonese | yue | 粤语 |

Citing HY-MT1.5:

``bibtex @misc{hy-mt1.5, title={HY-MT1.5 Technical Report}, author={Mao Zheng and Zheng Li and Tao Chen and Mingyang Song and Di Wang}, year={2025}, eprint={2512.24092}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2512.24092}, } ``