Upload README.md

README.md

@@ -43,109 +43,127 @@ language:
 
 
 <p align="center">
- <img src="https://dscache.tencent-cloud.cn/upload/uploader/hunyuan-64b418fd052c033b228e04bc77bbc4b54fd7f5bc.png" width="400"/> <br>
+ <img src="https://github.com/Tencent-Hunyuan/HY-MT/raw/main/imgs/hunyuanlogo.png" width="400"/> <br>
 </p><p></p>
 
 
 <p align="center">
-    🤗&nbsp;<a href="https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    🕹️&nbsp;<a href="https://hunyuan.tencent.com/modelSquare/home/list"><b>Demo</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    🤖&nbsp;<a href="https://modelscope.cn/collections/Hunyuan-MT-2ca6b8e1b4934f"><b>ModelScope</b></a>
+    🤗&nbsp;<a href="https://huggingface.co/collections/tencent/hy-mt15"><b>Hugging Face</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
+    🕹️&nbsp;<a href="https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=hunyuan-mt-1.8b"><b>Demo</b></a>&nbsp;&nbsp;&nbsp;&nbsp;
+    🤖&nbsp;<a href="https://modelscope.cn/collections/Tencent-Hunyuan/HY-MT15"><b>ModelScope</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
 </p>
 
 <p align="center">
     🖥️&nbsp;<a href="https://hunyuan.tencent.com"><b>Official Website</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    <a href="https://github.com/Tencent-Hunyuan/Hunyuan-MT"><b>GitHub</b></a>&nbsp;&nbsp;|&nbsp;&nbsp;
-    <a href="https://www.arxiv.org/abs/2509.05209"><b>Technical Report</b></a>
+    <a href="https://github.com/Tencent-Hunyuan/HY-MT"><b>Github</b></a>
 </p>
 
 
 ## Model Introduction
 
-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.
+Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality. After quantization, the 1.8B model can be deployed on edge devices and support real-time translation scenarios, making it widely applicable.
 
-### Key Features and Advantages
+## 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
+- HY-MT1.5-1.8B achieves the industry-leading performance among models of the same size, surpassing most commercial translation APIs.
+- HY-MT1.5-1.8B supports deployment on edge devices and real-time translation scenarios, offering broad applicability.
+- HY-MT1.5-7B, compared to its September open-source version, has been optimized for annotated and mixed-language scenarios.
+- Both models support terminology intervention, contextual translation, and formatted translation.
 
 ## Related News
-* 2025.9.1 We have open-sourced  **Hunyuan-MT-7B** , **Hunyuan-MT-Chimera-7B** on Hugging Face.
+* 2025.12.30, we have open-sourced **HY-MT1.5-1.8B** and **HY-MT1.5-7B** on Hugging Face.
+* 2025.9.1, we have open-sourced  **Hunyuan-MT-7B** , **Hunyuan-MT-Chimera-7B** on Hugging Face.
 <br>
 
 
+## Performance
+
+<div align='center'>
+<img src="https://github.com/Tencent-Hunyuan/HY-MT/raw/main/imgs/overall_performance.png" width = "80%" />
+</div>
+You can refer to our technical report for more experimental results and analysis.
+
+<a href="https://github.com/Tencent-Hunyuan/Hunyuan-MT/raw/main/HY_MT1_5_Technical_Report.pdf"><b>Technical Report</b> </a>
+
 &nbsp;
 
-## 模型链接
+## Model Links
 | Model Name  | Description | Download |
 | ----------- | ----------- |-----------
-| Hunyuan-MT-7B  | Hunyuan 7B translation model |🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B)|
-| Hunyuan-MT-7B-fp8 | Hunyuan 7B translation model，fp8 quant    | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-7B-fp8)|
-| Hunyuan-MT-Chimera | Hunyuan 7B translation ensemble model    | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B)|
-| Hunyuan-MT-Chimera-fp8 | Hunyuan 7B translation ensemble model，fp8 quant     | 🤗 [Model](https://huggingface.co/tencent/Hunyuan-MT-Chimera-7B-fp8)|
+| HY-MT1.5-1.8B  | Hunyuan 1.8B translation model |🤗 [Model](https://huggingface.co/tencent/HY-MT1.5-1.8B)|
+| HY-MT1.5-1.8B-FP8 | Hunyuan 1.8B translation model, fp8 quant    | 🤗 [Model](https://huggingface.co/tencent/HY-MT1.5-1.8B-FP8)|
+| HY-MT1.5-7B | Hunyuan 7B translation model    | 🤗 [Model](https://huggingface.co/tencent/HY-MT1.5-7B)|
+| HY-MT1.5-7B-FP8 | Hunyuan 7B translation model, fp8 quant     | 🤗 [Model](https://huggingface.co/tencent/HY-MT1.5-7B-FP8)|
 
 ## Prompts
 
 ### Prompt Template for ZH<=>XX Translation.
-
+---
 ```
+将以下文本翻译为{target_language}，注意只需要输出翻译后的结果，不要额外解释：
 
-把下面的文本翻译成<target_language>，不要额外解释。
-
-<source_text>
-
+{source_text}
 ```
+---
 
 ### Prompt Template for XX<=>XX Translation, excluding ZH<=>XX.
-
+---
 ```
+Translate the following segment into {target_language}, without additional explanation.
 
-Translate the following segment into <target_language>, without additional explanation.
-
-<source_text>
-
+{source_text}
 ```
+---
 
-### Prompt Template for Hunyuan-MT-Chmeria-7B
+### Prompt Template for terminology intervention.
+---
+```
+参考下面的翻译：
+{source_term} 翻译成 {target_term}
 
+将以下文本翻译为{target_language}，注意只需要输出翻译后的结果，不要额外解释：
+{source_text}
 ```
+---
 
-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.
+### Prompt Template for contextual translation.
+---
+```
+{context}
+参考上面的信息，把下面的文本翻译成{target_language}，注意不需要翻译上文，也不要额外解释：
+{source_text}
 
-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>```
+###  Prompt Template for formatted translation.
+---
+```
+将以下<source></source>之间的文本翻译为中文，注意只需要输出翻译后的结果，不要额外解释，原文中的<sn></sn>标签表示标签内文本包含格式信息，需要在译文中相应的位置尽量保留该标签。输出格式为：<target>str</target>
 
+<source>{src_text_with_format}</source>
 ```
+---
 
 &nbsp;
 
 ### Use with transformers
 First, please install transformers, recommends v4.56.0
 ```SHELL
-pip install transformers==v4.56.0
+pip install transformers==4.56.0
 ```
 
-The following code snippet shows how to use the transformers library to load and apply the model.
-
 *!!! 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.*
 
-we use tencent/Hunyuan-MT-7B for example
+The following code snippet shows how to use the transformers library to load and apply the model.
+
+we use tencent/HY-MT1.5-1.8B for example
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import os
 
-model_name_or_path = "tencent/Hunyuan-MT-7B"
+model_name_or_path = "tencent/HY-MT1.5-1.8B"
 
 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
@@ -174,6 +192,8 @@ We recommend using the following set of parameters for inference. Note that our
 }
 ```
 
+&nbsp;
+
 Supported languages:
 | Languages         | Abbr.   | Chinese Names   |
 |-------------------|---------|-----------------|
@@ -214,19 +234,4 @@ Supported languages:
 | Kazakh            | kk      | 哈萨克语        |
 | Mongolian         | mn      | 蒙古语          |
 | Uyghur            | ug      | 维吾尔语        |
-| Cantonese         | yue     | 粤语            |
-
-
-Citing Hunyuan-MT:
-
-```bibtex
-@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}, 
-}
-```
+| Cantonese         | yue     | 粤语            |