tencent

Hy-MT2-7B

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.

json
{
  "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.

json
{
  "temperature": 0.7,
  "top_p": 1.0,
  "top_k": -1,
  "repetition_penalty": 1.0,
  "max_tokens": 4096
}

transformers

transformers>=5.6.0

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "tencent/Hy-MT2-7B"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

model.eval()

# Example inference
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:

bash
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:

bash
vllm serve tencent/Hy-MT2-7B --tensor-parallel-size 1

sglang

Build SGLang from source:

bash
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:

bash
python3 -m sglang.launch_server --model tencent/Hy-MT2-7B --tp 1

llama_cpp

❕❕ This gguf depends on our STQ kernel, which is released at PR #22836.

Clone llama.cpp

bash
git clone https://github.com/ggml-org/llama.cpp.git

Enter the llama.cpp folder

bash
cd llama.cpp

Build llama.cpp

bash
cmake -B build
cmake --build build --config Release

Run a completion example

bash
./build/bin/llama-completion \
  --model model.gguf  \
  -p "Translate the following segment into Chinese, without additional explanation：Hello" \
  --jinja \
  -ngl 0 \
  -n 64 -st

Run the llama.cpp benchmark

bash
./build/bin/llama-bench -m model_zoo/model.gguf  -ngl 0

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

Quantization Tool

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

bibtex
@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}, 
}

Contact Us

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.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

tencent

Model Tree

Base

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Explore FriendliAI today

Get started Talk to an engineer

Model Introduction

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.

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)

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.

json
{
  "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.

json
{
  "temperature": 0.7,
  "top_p": 1.0,
  "top_k": -1,
  "repetition_penalty": 1.0,
  "max_tokens": 4096
}

transformers

transformers>=5.6.0

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "tencent/Hy-MT2-7B"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

model.eval()

# Example inference
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:

bash
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:

bash
vllm serve tencent/Hy-MT2-7B --tensor-parallel-size 1

sglang

Build SGLang from source:

bash
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:

bash
python3 -m sglang.launch_server --model tencent/Hy-MT2-7B --tp 1

llama_cpp

❕❕ This gguf depends on our STQ kernel, which is released at PR #22836.

Clone llama.cpp

bash
git clone https://github.com/ggml-org/llama.cpp.git

Enter the llama.cpp folder

bash
cd llama.cpp

Build llama.cpp

bash
cmake -B build
cmake --build build --config Release

Run a completion example

bash
./build/bin/llama-completion \
  --model model.gguf  \
  -p "Translate the following segment into Chinese, without additional explanation：Hello" \
  --jinja \
  -ngl 0 \
  -n 64 -st

Run the llama.cpp benchmark

bash
./build/bin/llama-bench -m model_zoo/model.gguf  -ngl 0

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

Quantization Tool

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

bibtex
@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}, 
}

Contact Us

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.

Hy-MT2-7B

README

Model Introduction

News

Results

Model Links

Hy-MT2 Translation Task Instruction Examples (Chinese-English Comparison)

Inference and Deployment

transformers

vllm

sglang

llama_cpp

Clone llama.cpp

Enter the llama.cpp folder

Build llama.cpp

Run a completion example

Run the llama.cpp benchmark

Model Training

Quantization Tool

Supported Languages

Citing Hy-MT2

Contact Us

Explore FriendliAI today

README

Model Introduction

News

Results

Model Links

Hy-MT2 Translation Task Instruction Examples (Chinese-English Comparison)

Inference and Deployment

transformers

vllm

sglang

llama_cpp

Clone llama.cpp

Enter the llama.cpp folder

Build llama.cpp

Run a completion example

Run the llama.cpp benchmark

Model Training

Quantization Tool

Supported Languages

Citing Hy-MT2

Contact Us