Model Introduction
Hy-MT2 is a family of “fast-thinking” multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages.
For on-device deployment, AngelSlim 1.25-bit extreme quantization reduces the storage requirement of the 1.8B model to only 440 MB and improves inference speed by 1.5x.
Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B-A3B models outperform open-source models such as DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking mode, while the lightweight 1.8B model also surpasses mainstream commercial APIs from providers such as Microsoft and Doubao overall.
In this release, we also open-source IFMTBench, a benchmark for evaluating translation instruction-following capabilities.
We also welcome everyone to use our released Hy-MT2-Translator Skill, which makes it easy to integrate Hy-MT2 series models for translation tasks. Download links: ClawHub and SkillHub.
Now, Tencent Hy is officially partnering with WMT26 for the "Video Subtitle Translation Task" (https://www2.statmt.org/wmt26/video-subtitle-translation.html). Participants who use the Hy-MT model series to compete in the "General Machine Translation Task" (https://www2.statmt.org/wmt26/translation-task.html) and the "Video Subtitle Translation Task" will have the chance to win special awards sponsored by Hunyuan. We sincerely invite everyone to participate and jointly push the boundaries of machine translation technology!
News
- 2026.5.21 We open-sourced Hy-MT2-1.8B/Hy-MT2-7B/Hy-MT2-30B-A3B/IFMTBench on HuggingFace and ModelScope.
- 2025.12.30 We open-sourced HY-MT1.5-1.8B and HY-MT1.5-7B on HuggingFace and ModelScope.
- 2025.9.1 We open-sourced Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B on HuggingFace and ModelScope.
Results
For more experimental results and analysis, please refer to our report.
Model Links
Table with columns: Model Name, Description, Download Link| Model Name | Description | Download Link |
|---|
| Hy-MT2-1.8B | Hy 1.8B translation model | 🤗 Model |
| Hy-MT2-1.8B-FP8 | Hy 1.8B translation model, FP8 quantization | 🤗 Model |
| Hy-MT2-1.8B-GGUF | Hy 1.8B translation model, llama.cpp | 🤗 Model |
| Hy-MT2-1.8B-2bit-GGUF | Hy 1.8B translation model, llama.cpp, 2bit |
Hy-MT2 Translation Task Instruction Examples (Chinese-English Comparison)
Note: In the following examples, both source_lang and target_lang should use the full language names. Chinese names should be used in Chinese prompts, and English names should be used in English prompts.
Table with columns: Type, Chinese prompt, English prompt| Type | Chinese prompt | English prompt |
|---|
| Default Translation | 将以下文本翻译为 {target_lang}，注意只需要输出翻译后的结果，不要额外解释：{source_text} | Translate the following text into {target_lang}. Note that you should only output the translated result without any additional explanation:{source_text} |
| Terminology | 参考下面的翻译：{text} 翻译成 {text}{text} 翻译成 {text}{text} 翻译成 将以下文本翻译为 ，注意： |
Inference and Deployment
For 1.8B and 7B, we recommend using the following parameters for inference. Note that our models do not have a default system_prompt.
{
"temperature": 0.7,
"top_p": 0.6,
"top_k": 20,
"repetition_penalty": 1.05,
"max_tokens": 4096
}
For 30B-A3B, we recommend using the following parameters for inference. Note that our models do not have a default system_prompt.
{
"temperature": 0.7,
"top_p": 1.0,
"top_k": -1,
"repetition_penalty": 1.0,
"max_tokens": 4096
}
transformers>=5.6.0
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_path = "tencent/Hy-MT2-1.8B"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_path,
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model.eval()
prompt = "将以下文本翻译成英语,注意只需要输出翻译后的结果,不要额外解释:\n\n今天天气真好。"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=4096,
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)
vllm
Build vLLM from source:
uv venv --python 3.12 --seed --managed-python
source .venv/bin/activate
git clone https://github.com/vllm-project/vllm.git
cd vllm
uv pip install --editable . --torch-backend=auto
Start the vLLM server:
vllm serve tencent/Hy-MT2-1.8B --tensor-parallel-size 1
sglang
Build SGLang from source:
git clone https://github.com/sgl-project/sglang
cd sglang
pip3 install pip --upgrade
pip3 install "transformers>=5.6.0"
pip3 install -e "python"
Launch SGLang server:
python3 -m sglang.launch_server --model tencent/Hy-MT2-1.8B --tp 1
Model Training
Hy-MT2 provides a complete model training pipeline, supporting both full-parameter fine-tuning and LoRA fine-tuning, as well as multiple DeepSpeed ZeRO configurations and LLaMA-Factory integration.
For detailed training documentation, please refer to: Model Training Guide
We provide AngelSlim, an easy-to-use, comprehensive, and efficient large model compression toolkit covering common quantization algorithms, low-bit quantization, speculative sampling, and more.
Supported Languages
Table with columns: Languages, Abbr., Chinese Names| 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 |
Citing Hy-MT2
@misc{zheng2026hymt2familyfastefficient,
title={Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild},
author={Mao Zheng and Zheng Li and Tao Chen and Bo Lv and Mingrui Sun and Mingyang Song and Jinlong Song and Hong Huang and Decheng Wu and Hai Wang and Yifan Song and Yanfeng Chen and Guanwei Zhang},
year={2026},
eprint={2605.22064},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2605.22064},
}
If you would like to leave feedback for our R&D and product teams, you are welcome to contact the Tencent Hunyuan LLM team. You can reach us by email at hunyuan_opensource@tencent.com.
