HY-MT1.5
Collection
混元翻译模型1.5版本
•
8 items
•
Updated
•
19
🤗 Hugging Face | 🕹️ Demo 🤖 ModelScope |
🖥️ Official Website | 🪡 AngelSlim | 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.
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
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
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.
{
"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 |
- Downloads last month
- -