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README.md

@@ -1,3 +1,300 @@
 ---
+library_name: transformers
+tags:
+- translation
+language:
+- zh
+- en
+- fr
+- pt
+- es
+- ja
+- tr
+- ru
+- ar
+- ko
+- th
+- it
+- de
+- vi
+- ms
+- id
+- tl
+- hi
+- pl
+- cs
+- nl
+- km
+- my
+- fa
+- gu
+- ur
+- te
+- mr
+- he
+- bn
+- ta
+- uk
+- bo
+- kk
+- mn
+- ug
 license: apache-2.0
 ---
+
+
+<p align="left">
+   English&nbsp;｜&nbsp;<a href="https://huggingface.co/tencent/Hy-MT2-30B-A3B/blob/main/README_CN.md">中文</a>
+</p>
+<br>
+
+<p align="center">
+ <img src="imgs/logo-en.png" width="400"/> <br> 
+</p>
+
+<div align="center" style="line-height: 1;">
+
+
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Tencent%20Hy-ffc107?color=ffc107&logoColor=white)](https://huggingface.co/collections/tencent/hy-mt2)
+&nbsp;&nbsp;
+[![ModelScope](https://img.shields.io/badge/ModelScope-Tencent%20Hy-624aff)](https://modelscope.cn/collections/Tencent-Hunyuan/Hy-MT2)
+
+</div>
+
+<p align="center">
+    🖥️&nbsp;<a href="https://aistudio.tencent.com/llm/en?tabIndex=0"><b>Official Website</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    💬&nbsp;<a href="https://github.com/Tencent-Hunyuan/Hy-MT2"><b>GitHub</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    🪡&nbsp;<a href="https://github.com/Tencent/AngelSlim/tree/main"><b>AngelSlim</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    📚&nbsp;<a href="https://arxiv.org/pdf/2605.22064"><b>Hy-MT2 Report</b></a></p>
+
+## 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](./IFMTBench/README.md), 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](https://clawhub.ai/tencent-adm/hy-mt2-translator-skill) and [SkillHub](https://skillhub.cn/skills/hy-mt2-translator).
+
+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
+<div align='center'>
+<img src="imgs/main_result.png" width = "100%" />
+</div>
+
+For more experimental results and analysis, please refer to our [report](https://arxiv.org/pdf/2605.22064).
+
+&nbsp;
+
+## Model Links
+| Model Name  | Description | Download Link |
+| ----------- | ----------- |-----------
+| Hy-MT2-1.8B  | Hy 1.8B translation model |🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B)|
+| Hy-MT2-1.8B-FP8 | Hy 1.8B translation model, FP8 quantization    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-FP8)|
+| Hy-MT2-1.8B-GGUF | Hy 1.8B translation model, llama.cpp    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-GGUF)|
+| Hy-MT2-1.8B-2bit-GGUF | Hy 1.8B translation model, llama.cpp, 2bit    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-2bit-GGUF)|
+| Hy-MT2-1.8B-1.25bit-GGUF | Hy 1.8B translation model, llama.cpp, 1.25bit    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-1.25bit-GGUF)|
+| Hy-MT2-7B | Hy 7B translation model    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B)|
+| Hy-MT2-7B-FP8 | Hy 7B translation model, FP8 quantization     | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B-FP8)|
+| Hy-MT2-7B-GGUF | Hy 7B translation model, llama.cpp    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B-GGUF)|
+| Hy-MT2-30B-A3B | Hy 30B-A3B translation model    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-30B-A3B)|
+| Hy-MT2-30B-A3B-FP8 | Hy 30B-A3B translation model, FP8 quantization     | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-30B-A3B-FP8)|
+
+
+
+## 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.*
+
+| 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**：<br><br>`{source_text}` | Translate the following text into `{target_lang}`. Note that you should **only output the translated result without any additional explanation**:<br><br>`{source_text}` |
+| **Terminology** | *参考下面的翻译：*<br>`{text}` 翻译成 `{text}`<br>`{text}` 翻译成 `{text}`<br>`{text}` 翻译成 `{text}`<br>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**：<br><br>`{source_text}` | *Reference the following translations:*<br>`{text}` translates to `{text}`<br>`{text}` translates to `{text}`<br>`{text}` translates to `{text}`<br><br>Translate the following text into `{target_lang}`. Note that you must **ONLY output the translated result without any additional explanation**:<br><br>`{source_text}` |
+| **Style** | 请将以下文本翻译为 `{target_lang}`。<br>注意翻译的风格要严格符合【**`{target_style}`**】<br><br>`{source_text}` | Please translate the following text into `{target_lang}`. Note that the translation style must strictly conform to [**`{target_style}`**]:<br><br>`{source_text}` |
+| **Personalization** | *【待翻译文本】*<br>`{source_text}`<br><br>*【翻译任务】*<br>1、**`{user_preferences}`**<br>2、**`{user_preferences}`**<br>3、……<br>4、将【待翻译文本】翻译为 `{target_lang}`。 | *[Source Text]*<br>`{source_text}`<br><br>*[Translation Tasks]*<br>1. **`{user_preferences}`**<br>2. **`{user_preferences}`**<br>3. ...<br>4. Translate the [Source Text] into `{target_lang}`. |
+| **Delimiters** | 请将以下文本准确翻译为 `{target_lang}`。<br>你必须在译文中**保留等量的分隔符，绝对不可遗漏、转义或翻译该符号，并注意分隔符的位置**。<br><br>`{source_text}` | Please accurately translate the following text into `{target_lang}`.<br>You must **retain the exact same number of delimiters in the translation. Strictly do not omit, escape, or translate these symbols, and pay close attention to their placement**.<br><br>`{source_text}` |
+| **Structured Data 1** | *# 任务目标*<br>将下方 `{source_text}` 中的 `{format_type}` 格式数据翻译为 `{target_lang}`。<br><br>*# 严格约束*<br>1. **结构锁定**：绝对保持原有的 `{format_type}` 数据结构、缩进和层级完全不变。<br>2. **选择性翻译**：仅翻译面向用户展示的可见文本内容。<br>3. **禁止修改**：**严禁**翻译或更改任何代码标签、键名 (Key)、变量占位符（如 `{{var}}`、`${var}`、`%s`、`%d` 等）或代码属性。<br><br>*# 数据输入*<br>`{source_text}` | *### Task*<br>Translate the user-facing text within the following `{format_type}` data into `{target_lang}`.<br><br>*### Strict Rules*<br>1. **Structure Preservation:** You MUST preserve the original `{format_type}` data structure, nesting, hierarchy, and indentation exactly as they are.<br>2. **Selective Translation:** Translate ONLY the visible, user-facing text content/values.<br>3. **Strict Non-Translation:** NEVER translate or alter code tags, keys, properties, object names, or variable placeholders. Leave them exactly in their original English/code form.<br><br>*### Source Data*<br>`{source_text}` |
+| **Structured Data 2** | *【背景信息】*<br>`{background_text}`<br><br>请结合背景信息将以下文本翻译为 `{target_lang}`。<br><br>*【待翻译文本】*<br>`{source_text}` | *[Background Information]*<br>`{background_text}`<br><br>Please translate the following text into `{target_lang}`, taking the provided background information into consideration.<br><br>*[Source Text]*<br>`{source_text}` |
+
+---
+
+## 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-MT2-30B-A3B"
+
+# 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-MT2-30B-A3B --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-MT2-30B-A3B --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](./train/README.md)
+
+## Quantization Tool
+
+We provide [AngelSlim](https://github.com/tencent/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
+| 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            |
+
+
+
+## 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.

README_CN.md

@@ -0,0 +1,254 @@
+<p align="left">
+   <a href="README.md">English</a>&nbsp;｜&nbsp;中文
+</p>
+<br>
+
+<p align="center">
+ <img src="imgs/logo-zh.png" width="400"/> <br> 
+</p>
+
+<div align="center" style="line-height: 1;">
+
+
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Tencent%20Hy-ffc107?color=ffc107&logoColor=white)](https://huggingface.co/collections/tencent/hy-mt2)
+&nbsp;&nbsp;
+[![ModelScope](https://img.shields.io/badge/ModelScope-Tencent%20Hy-624aff)](https://modelscope.cn/collections/Tencent-Hunyuan/Hy-MT2)
+
+</div>
+
+<p align="center">
+    🖥️&nbsp;<a href="https://aistudio.tencent.com/llm/zh?tabIndex=0"><b>官方网站</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    💬&nbsp;<a href="https://github.com/Tencent-Hunyuan/Hy-MT2"><b>GitHub</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    🪡&nbsp;<a href="https://github.com/Tencent/AngelSlim/tree/main"><b>AngelSlim</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    📚&nbsp;<a href="https://arxiv.org/pdf/2605.22064"><b>Hy-MT2报告</b></a></p>
+
+
+## 模型介绍
+
+
+Hy-MT2 是一款面向真实复杂场景的“快思考”多语言翻译模型家族，涵盖 1.8B、7B 和 30B-A3B（MoE）三种体量，支持 33 种语言互译并具备强大的多语言指令遵循能力。在端侧部署上，得益于 AngelSlim 1.25-bit 极端量化，其 1.8B 模型仅需 440MB 存储空间，推理速度显著提升 1.5 倍。多维度评测表明，Hy-MT2 在通用、真实业务、专业领域及指令遵循等翻译任务中表现卓越：7B 和 30B-A3B 模型性能不仅超越了 DeepSeek-V4-Pro、Kimi K2.6 等开源模型在快思考模式下的表现，轻量级 1.8B 模型亦在整体上超越了微软和豆包等主流商业 API。
+
+同时，本次我们也开源了一个针对翻译指令遵循能力的评测集[IFMTBench](./IFMTBench/README_zh.md)。
+
+也欢迎大家使用我们发布的 Hy-MT2-Translator Skill，可以方便接入Hy-MT2系列模型完成翻译任务，下载链接[ClawHub](https://clawhub.ai/tencent-adm/hy-mt2-translator-skill)和[SkillHub](https://skillhub.cn/skills/hy-mt2-translator)。
+
+现在，腾讯混元也在与WMT26官方合作「视频字幕翻译比赛」（https://www2.statmt.org/wmt26/video-subtitle-translation.html ），使用Hy-MT系列模型参与「通用机器翻译比赛」（https://www2.statmt.org/wmt26/translation-task.html ）和「视频字幕翻译比赛」有机会获得混元特设奖励，诚邀邀大家参与，共同推动机器翻译前沿技术发展。
+
+## 新闻
+
+* 2026.5.21  我们在HuggingFace和ModelScope上开源了 **Hy-MT2-1.8B**/**Hy-MT2-7B**/**Hy-MT2-30B-A3B**/**IFMTBench**
+* 2025.12.30 我们在HuggingFace和ModelScope开源了 **HY-MT1.5-1.8B**和**HY-MT1.5-7B**
+* 2025.9.1 我们在HuggingFace和ModelScope开源了 **Hunyuan-MT-7B**和**Hunyuan-MT-Chimera-7B**。
+
+
+## 效果
+<div align='center'>
+<img src="imgs/main_result.png" width = "100%" />
+</div>
+
+更多的实验效果和分析可以参考我们的[报告](https://arxiv.org/pdf/2605.22064)。
+
+&nbsp;
+
+## 模型链接
+| 模型名  | 简介 | 下载链接 |
+| ----------- | ----------- |-----------
+| Hy-MT2-1.8B  | 混元1.8B翻译模型 |🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B)|
+| Hy-MT2-1.8B-FP8 | 混元1.8B翻译模型，fp8量化    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-FP8)|
+| Hy-MT2-1.8B-GGUF | 混元1.8B翻译模型， llama.cpp    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-GGUF)|
+| Hy-MT2-1.8B-2bit-GGUF | 混元1.8B翻译模型， llama.cpp, 2bit    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-2bit-GGUF)|
+| Hy-MT2-1.8B-1.25bit-GGUF | 混元1.8B翻译模型， llama.cpp, 1.25bit    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-1.8B-1.25bit-GGUF)|
+| Hy-MT2-7B | 混元7B翻译模型    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B)|
+| Hy-MT2-7B-FP8 | 混元7B翻译模型，fp8量化     | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B-FP8)|
+| Hy-MT2-7B-GGUF | 混元7B翻译模型， llama.cpp    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-7B-GGUF)|
+| Hy-MT2-30B-A3B | 混元30B-A3B翻译模型    | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-30B-A3B)|
+| Hy-MT2-30B-A3B-FP8 | 混元30B-A3B翻译模型，fp8量化     | 🤗 [Model](https://huggingface.co/tencent/Hy-MT2-30B-A3B-FP8)|
+
+
+
+## Hy-MT2 翻译任务指令示例（中英文对照）
+
+*注意：下面的source_lang和target_lang都使用语言的全称，中文使用中文全称，英文使用英文全称。*
+
+| 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**：<br><br>`{source_text}` | Translate the following text into `{target_lang}`. Note that you should **only output the translated result without any additional explanation**:<br><br>`{source_text}` |
+| **Terminology** | *参考下面的翻译：*<br>`{text}` 翻译成 `{text}`<br>`{text}` 翻译成 `{text}`<br>`{text}` 翻译成 `{text}`<br>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**：<br><br>`{source_text}` | *Reference the following translations:*<br>`{text}` translates to `{text}`<br>`{text}` translates to `{text}`<br>`{text}` translates to `{text}`<br><br>Translate the following text into `{target_lang}`. Note that you must **ONLY output the translated result without any additional explanation**:<br><br>`{source_text}` |
+| **Style** | 请将以下文本翻译为 `{target_lang}`。<br>注意翻译的风格要严格符合【**`{target_style}`**】<br><br>`{source_text}` | Please translate the following text into `{target_lang}`. Note that the translation style must strictly conform to [**`{target_style}`**]:<br><br>`{source_text}` |
+| **Personalization** | *【待翻译文本】*<br>`{source_text}`<br><br>*【翻译任务】*<br>1、**`{user_preferences}`**<br>2、**`{user_preferences}`**<br>3、……<br>4、将【待翻译文本】翻译为 `{target_lang}`。 | *[Source Text]*<br>`{source_text}`<br><br>*[Translation Tasks]*<br>1. **`{user_preferences}`**<br>2. **`{user_preferences}`**<br>3. ...<br>4. Translate the [Source Text] into `{target_lang}`. |
+| **Delimiters** | 请将以下文本准确翻译为 `{target_lang}`。<br>你必须在译文中**保留等量的分隔符，绝对不可遗漏、转义或翻译该符号，并注意分隔符的位置**。<br><br>`{source_text}` | Please accurately translate the following text into `{target_lang}`.<br>You must **retain the exact same number of delimiters in the translation. Strictly do not omit, escape, or translate these symbols, and pay close attention to their placement**.<br><br>`{source_text}` |
+| **Structured Data 1** | *# 任务目标*<br>将下方 `{source_text}` 中的 `{format_type}` 格式数据翻译为 `{target_lang}`。<br><br>*# 严格约束*<br>1. **结构锁定**：绝对保持原有的 `{format_type}` 数据结构、缩进和层级完全不变。<br>2. **选择性翻译**：仅翻译面向用户展示的可见文本内容。<br>3. **禁止修改**：**严禁**翻译或更改任何代码标签、键名 (Key)、变量占位符（如 `{{var}}`、`${var}`、`%s`、`%d` 等）或代码属性。<br><br>*# 数据输入*<br>`{source_text}` | *### Task*<br>Translate the user-facing text within the following `{format_type}` data into `{target_lang}`.<br><br>*### Strict Rules*<br>1. **Structure Preservation:** You MUST preserve the original `{format_type}` data structure, nesting, hierarchy, and indentation exactly as they are.<br>2. **Selective Translation:** Translate ONLY the visible, user-facing text content/values.<br>3. **Strict Non-Translation:** NEVER translate or alter code tags, keys, properties, object names, or variable placeholders. Leave them exactly in their original English/code form.<br><br>*### Source Data*<br>`{source_text}` |
+| **Structured Data 2** | *【背景信息】*<br>`{background_text}`<br><br>请结合背景信息将以下文本翻译为 `{target_lang}`。<br><br>*【待翻译文本】*<br>`{source_text}` | *[Background Information]*<br>`{background_text}`<br><br>Please translate the following text into `{target_lang}`, taking the provided background information into consideration.<br><br>*[Source Text]*<br>`{source_text}` |
+
+---
+
+## 推理和部署
+
+对于1.8B和7B，我们推荐使用下面这组参数进行推理。注意，我们的模型没有默认 system_prompt。
+
+```json
+
+{
+  "temperature": 0.7,
+  "top_p": 0.6,
+  "top_k": 20,
+  "repetition_penalty": 1.05,
+  "max_tokens": 4096
+}
+```
+
+对于30B-A3B，我们推荐使用下面这组参数进行推理。注意，我们的模型没有默认 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-MT2-30B-A3B"
+
+# 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-MT2-30B-A3B --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-MT2-30B-A3B --tp 1
+```
+
+
+## 模型训练
+Hy-MT2提供了完整的模型训练流程，支持全量微调和 LoRA 微调，同时支持 DeepSpeed ZeRO 多种配置以及 LLaMA-Factory 集成。
+
+详细的训练文档请参考：[模型训练指南](./train/README_CN.md)
+
+## 量化工具
+
+我们提供了 [AngelSlim](https://github.com/tencent/AngelSlim)——一套易用、全面、高效的大模型压缩工具包，涵盖常用量化算法、低比特量化和投机采样等能力。
+
+
+## 支持的语种
+| 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            |
+
+
+
+## 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}, 
+} 
+```
+
+## 联系我们
+如果你想给我们的研发和产品团队留言，欢迎联系我们腾讯混元LLM团队。你可以通过邮件（hunyuan_opensource@tencent.com）联系我们。