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🖥️ Official Website | 💬 GitHub | 🪡 AngelSlim | 📚 Hy-MT2 Report
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!
For more experimental results and analysis, please refer to our report.
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.
For 1.8B and 7B, we recommend using the following parameters for inference. Note that our models do not have a default system_prompt.
For 30B-A3B, we recommend using the following parameters for inference. Note that our models do not have a default system_prompt.
transformers>=5.6.0
Build vLLM from source:
Start the vLLM server:
Build SGLang from source:
Launch SGLang server:
❕❕ This gguf depends on our STQ kernel, which is released at PR #22836.
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.
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.
{ "temperature": 0.7, "top_p": 0.6, "top_k": 20, "repetition_penalty": 1.05, "max_tokens": 4096}
{ "temperature": 0.7, "top_p": 1.0, "top_k": -1, "repetition_penalty": 1.0, "max_tokens": 4096}from transformers import AutoModelForCausalLM, AutoTokenizerimport torch
model_path = "tencent/Hy-MT2-7B-FP8"
# Load tokenizertokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# Load modelmodel = AutoModelForCausalLM.from_pretrained( model_path, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,)
model.eval()
# Example inferenceprompt = "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)uv venv --python 3.12 --seed --managed-pythonsource .venv/bin/activategit clone https://github.com/vllm-project/vllm.gitcd vllmuv pip install --editable . --torch-backend=autovllm serve tencent/Hy-MT2-7B-FP8 --tensor-parallel-size 1git clone https://github.com/sgl-project/sglangcd sglangpip3 install pip --upgradepip3 install "transformers>=5.6.0"pip3 install -e "python"python3 -m sglang.launch_server --model tencent/Hy-MT2-7B-FP8 --tp 1git clone https://github.com/ggml-org/llama.cpp.gitcd llama.cppcmake -B buildcmake --build build --config Release./build/bin/llama-completion \ --model model.gguf \ -p "Translate the following segment into Chinese, without additional explanation：Hello" \ --jinja \ -ngl 0 \ -n 64 -st ./build/bin/llama-bench -m model_zoo/model.gguf -ngl 0@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}, }