The Hunyuan Translation Model 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 → cross-lingual pretraining (CPT) → supervised fine-tuning (SFT) → translation enhancement → ensemble refinement, 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.
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-Chmeria-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 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.
Runs of tencent Hunyuan-MT-7B-fp8 on huggingface.co
176
Total runs
0
24-hour runs
-9
3-day runs
-12
7-day runs
-243
30-day runs
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