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tencent/
HY-MT1.5-1.8B

Name: HY-MT1.5-1.8B
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🤗 Hugging Face | 🕹️ Demo 🤖 ModelScope |

🖥️ Official Website | Github

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.

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 ZH<=>XX 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 XX<=>XX Translation, excluding ZH<=>XX.

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<source></source>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<sn></sn>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：<target>str</target>

<source>{src_text_with_format}</source>

Use with transformers

First, please install transformers, recommends v4.56.0

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}, 
}

tencent/HY-MT1.5-1.8B