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Model Introduction

The Hunyuan-MT comprises a translation model, Hunyuan-MT-7B, and an ensemble model, Hunyuan-MT-Chimera. The translation model is used to translate source text into the target language, while the ensemble model integrates multiple translation outputs to produce a higher-quality result. It primarily supports mutual translation among 33 languages, including five ethnic minority languages in China.

Key Features and Advantages

• In the WMT25 competition, the model achieved first place in 30 out of the 31 language categories it participated in.
• Hunyuan-MT-7B achieves industry-leading performance among models of comparable scale
• Hunyuan-MT-Chimera-7B is the industry’s first open-source translation ensemble model, elevating translation quality to a new level
• A comprehensive training framework for translation models has been proposed, spanning from pretrain → continue pretraining (CPT) → supervised fine-tuning (SFT) → translation rl → ensemble rl, achieving state-of-the-art (SOTA) results for models of similar size

Related News

• 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
Hunyuan-MT-7B	Hunyuan 7B translation model	🤗 Model
Hunyuan-MT-7B-fp8	Hunyuan 7B translation model，fp8 quant	🤗 Model
Hunyuan-MT-Chimera	Hunyuan 7B translation ensemble model	🤗 Model
Hunyuan-MT-Chimera-fp8	Hunyuan 7B translation ensemble model，fp8 quant	🤗 Model

Prompts

Prompt Template for ZH<=>XX Translation.

把下面的文本翻译成<target_language>，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 Hunyuan-MT-Chimera-7B

Analyze the following multiple <target_language> translations of the <source_language> segment surrounded in triple backticks and generate a single refined <target_language> translation. Only output the refined translation, do not explain.

The <source_language> segment:
```<source_text>```

The multiple `<target_language>` translations:
1. ```<translated_text1>```
2. ```<translated_text2>```
3. ```<translated_text3>```
4. ```<translated_text4>```
5. ```<translated_text5>```
6. ```<translated_text6>```

 

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/Hunyuan-MT-7B for example

from transformers import AutoModelForCausalLM, AutoTokenizer
import os

model_name_or_path = "tencent/Hunyuan-MT-7B"

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

Training Data Format

If you need to fine-tune our Instruct model, we recommend processing the data into the following format.

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Why is seawater salty?" },
    {"role": "assistant", "content": "Seawater is primarily saline due to dissolved salts and minerals. These substances come from the chemical materials in rocks and soil on the Earth's surface, which are carried into the ocean over time. When seawater evaporates, the water vapor leaves, but the salts and minerals remain, making the seawater saltier. Therefore, the salinity of seawater is determined by the amount of salts and minerals it contains."}
]

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("your_tokenizer_path", trust_remote_code=True)
train_ids = tokenizer.apply_chat_template(messages)

 

Train with LLaMA-Factory

In the following chapter, we will introduce how to use LLaMA-Factory to fine-tune the Hunyuan model.

Prerequisites

Verify installation of the following dependencies:

• LLaMA-Factory: Follow official installation guide
• DeepSpeed (optional): Follow official installation guide
• Transformer Library: Use the companion branch (Hunyuan-submitted code is pending review)

  pip install git+https://github.com/huggingface/transformers@4970b23cedaf745f963779b4eae68da281e8c6ca
  

Data preparation

We need to prepare a custom dataset:

1. Organize your data in json format and place it in the data directory in LLaMA-Factory. The current implementation uses the sharegpt dataset format, which requires the following structure:

[
  {
    "messages": [
      {
        "role": "system",
        "content": "System prompt (optional)"
      },
      {
        "role": "user",
        "content": "Human instruction"
      },
      {
        "role": "assistant",
        "content": "Model response"
      }
    ]
  }
]

Refer to the Data Format section mentioned earlier for details.

2. Define your dataset in the data/dataset_info.json file using the following format:

"dataset_name": {
  "file_name": "dataset.json",
  "formatting": "sharegpt",
  "columns": {
    "messages": "messages"
  },
  "tags": {
    "role_tag": "role",
    "content_tag": "content",
    "user_tag": "user",
    "assistant_tag": "assistant",
    "system_tag": "system"
  }
}

Training execution

1. Copy all files from the llama_factory_support/example_configs directory to the example/hunyuan directory in LLaMA-Factory.
2. Modify the model path and dataset name in the configuration file hunyuan_full.yaml. Adjust other configurations as needed:

### model
model_name_or_path: [!!!add the model path here!!!]

### dataset
dataset: [!!!add the dataset name here!!!]

3. Execute training commands: *​​Single-node training​​ Note: Set the environment variable DISABLE_VERSION_CHECK to 1 to avoid version conflicts.

   export DISABLE_VERSION_CHECK=1
   llamafactory-cli train examples/hunyuan/hunyuan_full.yaml
   

   *Multi-node training​​ Execute the following command on each node. Configure NNODES, NODE_RANK, MASTER_ADDR, and MASTER_PORT according to your environment:

   export DISABLE_VERSION_CHECK=1
   FORCE_TORCHRUN=1 NNODES=${NNODES} NODE_RANK=${NODE_RANK} MASTER_ADDR=${MASTER_ADDR} MASTER_PORT=${MASTER_PORT} \
   llamafactory-cli train examples/hunyuan/hunyuan_full.yaml
   

 

Quantization Compression

We used our own AngelSlim compression tool to produce FP8 and INT4 quantization models. AngelSlim is a toolset dedicated to creating a more user-friendly, comprehensive and efficient model compression solution.

FP8 Quantization

We use FP8-static quantization, FP8 quantization adopts 8-bit floating point format, through a small amount of calibration data (without training) to pre-determine the quantization scale, the model weights and activation values will be converted to FP8 format, to improve the inference efficiency and reduce the deployment threshold. We you can use AngelSlim quantization, you can also directly download our quantization completed open source model to use AngelSlim.

Deployment

For deployment, you can use frameworks such as TensorRT-LLM, vLLM, or SGLang to serve the model and create an OpenAI-compatible API endpoint.

image: https://hub.docker.com/r/hunyuaninfer/hunyuan-7B/tags

TensorRT-LLM

Docker Image

We provide a pre-built Docker image based on the latest version of TensorRT-LLM.

We use tencent/Hunyuan-7B-Instruct for example

• To get started:

docker pull docker.cnb.cool/tencent/hunyuan/hunyuan-7b:hunyuan-7b-trtllm

docker run --privileged --user root --name hunyuanLLM_infer --rm -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --gpus=all hunyuaninfer/hunyuan-7b:hunyuan-7b-trtllm

• Prepare Configuration file:

cat >/path/to/extra-llm-api-config.yml <<EOF
use_cuda_graph: true
cuda_graph_padding_enabled: true
cuda_graph_batch_sizes:
- 1
- 2
- 4
- 8
- 16
- 32
print_iter_log: true
EOF

• Start the API server:

trtllm-serve \
  /path/to/HunYuan-7b \
  --host localhost \
  --port 8000 \
  --backend pytorch \
  --max_batch_size 32 \
  --max_num_tokens 16384 \
  --tp_size 2 \
  --kv_cache_free_gpu_memory_fraction 0.6 \
  --trust_remote_code \
  --extra_llm_api_options /path/to/extra-llm-api-config.yml

vllm

Start

Please use vLLM version v0.10.0 or higher for inference.

First, please install transformers. We will merge it into the main branch later.

pip install git+https://github.com/huggingface/transformers@4970b23cedaf745f963779b4eae68da281e8c6ca

We use tencent/Hunyuan-7B-Instruct for example

• Download Model file:

  • Huggingface: will download automicly by vllm.
  • ModelScope: modelscope download --model Tencent-Hunyuan/Hunyuan-7B-Instruct

• model download by huggingface:

export MODEL_PATH=tencent/Hunyuan-7B-Instruct

• model downloaded by modelscope:

export MODEL_PATH=/root/.cache/modelscope/hub/models/Tencent-Hunyuan/Hunyuan-7B-Instruct/

• Start the API server:

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --quantization experts_int8 \
    --served-model-name hunyuan \
    2>&1 | tee log_server.txt

• After running service script successfully, run the request script

curl http://0.0.0.0:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "hunyuan",
"messages": [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [{"type": "text", "text": "请按面积大小对四大洋进行排序，并给出面积最小的洋是哪一个？直接输出结果。"}]
    }
],
"max_tokens": 2048,
"temperature":0.7,
"top_p": 0.6,
"top_k": 20,
"repetition_penalty": 1.05,
"stop_token_ids": [127960]
}'

Quantitative model deployment

This section describes the process of deploying a post-quantization model using vLLM.

Default server in BF16.

Int8 quantitative model deployment

Deploying the Int8-weight-only version of the HunYuan-7B model only requires setting the environment variables

Next we start the Int8 service. Run:

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --quantization experts_int8 \
    2>&1 | tee log_server.txt

Int4 quantitative model deployment

Deploying the Int4-weight-only version of the HunYuan-7B model only requires setting the environment variables , using the GPTQ method

export MODEL_PATH=PATH_TO_INT4_MODEL

Next we start the Int4 service. Run

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --quantization gptq_marlin \
    2>&1 | tee log_server.txt

FP8 quantitative model deployment

Deploying the W8A8C8 version of the HunYuan-7B model only requires setting the environment variables

Next we start the FP8 service. Run

python3 -m vllm.entrypoints.openai.api_server \
    --host 0.0.0.0 \
    --port 8000 \
    --trust-remote-code \
    --model ${MODEL_PATH} \
    --tensor-parallel-size 1 \
    --dtype bfloat16 \
    --served-model-name hunyuan \
    --kv-cache-dtype fp8 \
    2>&1 | tee log_server.txt

SGLang

Docker Image

We also provide a pre-built Docker image based on the latest version of SGLang.

We use tencent/Hunyuan-7B-Instruct for example

To get started:

• Pull the Docker image

docker pull lmsysorg/sglang:latest

• Start the API server:

docker run --entrypoint="python3" --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    --ulimit nproc=10000 \
    --privileged \
    --ipc=host \
     lmsysorg/sglang:latest \
    -m sglang.launch_server --model-path hunyuan/huanyuan_7B --tp 4 --trust-remote-code --host 0.0.0.0 --port 30000

Citing Hunyuan-MT:

@misc{hunyuan_mt,
      title={Hunyuan-MT Technical Report}, 
      author={Mao Zheng and Zheng Li and Bingxin Qu and Mingyang Song and Yang Du and Mingrui Sun and Di Wang},
      year={2025},
      eprint={2509.05209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.05209}, 
}

Contact Us

If you would like to leave a message for our R&D and product teams, Welcome to contact our open-source team . You can also contact us via email ([email protected]).