Tencent-Hunyuan/Hy-MT

Tencent-Hunyuan/Hy-MT

Language: Python

License: NOASSERTION

Stars: 776

Forks: 70

Open issues: 24

Created: 2025-12-26T08:28:42Z

Pushed: 2026-06-01T11:38:04Z

Default branch: main

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README:

中文&nbsp ｜ English

🤗 Hugging Face | ModelScope |

🖥️ Official Website | 🕹️ Demo

Github

NOTICE:

We have released the Hy-MT2 series of translation models, offering improved performance and excellent instruction-following capabilities. The link to the new model collection is: https://huggingface.co/collections/tencent/hy-mt2

We are excited to announce our official partnership with WMT26. We welcome all participants to use our HY-MT model during the competition. Teams that use HY-MT and achieve notable results will be eligible for cash prizes. For more details, please contact us at hunyuan@tencent.com.

To help you get started with HY-MT training more quickly, we have provided a Training Tutorial. You can access it via the [link](#Train-with-LLaMA-Factory).

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-MT1.5-1.8B-GGUF | Hunyuan 1.8B translation model, llama.cpp | 🤗 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-GGUF | Hunyuan 7B translation model, llama.cpp | 🤗 Model|

Prompts

*Note: The following source_language and target_language should both use the full names of the languages; use the full Chinese names for Chinese instruction and the full English names for English instruction.*

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

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…