Update README.md

README.md

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----
-license: other
-license_name: openmdw
-license_link: LICENSE
----
+---
+license: other
+license_name: openmdw
+license_link: LICENSE
+---
+# Seed-X-PPO-7B
+<a href="https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/Technical_Report.pdf">
+  <img src="https://img.shields.io/badge/Seed--X-Report-blue"></a>
+<a href="XXXX">
+  <img src="https://img.shields.io/badge/Seed--X-Hugging Face-brightgreen"></a>
+<a href="https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/LICENSE.openmdw">
+  <img src="https://img.shields.io/badge/License-OpenMDW-yellow"></a>
+
+## Introduction
+We are excited to introduce **Seed-X**, a powerful open-source multilingual translation language model series, including instruction and reasoning models, with 7B parameters pushing the boundaries of translation capabilities.
+We develop Seed-X as an accessible, off-the-shelf tool to support the community in advancing translation research and applications:
+* **Exceptional translation capabilities**: Seed-X exhibits state-of-the-art translation capabilities, on par with or outperforming ultra-large models like Gemini-2.5, Claude-3.5, and GPT-4, as validated by human evaluations and automatic metrics.
+* **Deployment and inference-friendly**: With a compact 7B parameter count and mistral architecture, Seed-X offers outstanding translation performance in a lightweight and efficient package, ideal for deployment and inference.
+* **Broad domain coverage**: Seed-X excels on a highly challenging translation test set spanning diverse domains, including the internet, science and technology, office dialogues, e-commerce, biomedicine, finance, law, literature, and entertainment.
+![performance](imgs/model_comparsion.png)
+
+This repo contains the **Seed-X-Instruct** model, with the following features:
+* Type: Causal language models
+* Training Stage: Pretraining & Post-training
+* Support: Multilingual translation among 28 languages
+
+| Languages  | Abbr. | Languages  | Abbr. | Languages  | Abbr. | Languages  | Abbr. |
+| ----------- | ----------- |-----------|-----------|-----------|-----------| -----------|-----------|
+|Arabic | ar  |French              | fr  | Malay            |  ms  | Russian                   | ru                                | 
+|Czech  | cs  |Croatian            | hr  | Norwegian Bokmal |  nb                   | Swedish  | sv                | 
+|Danish  | da |Hungarian           | hu  |  Dutch           | nl                    |  Thai                      | th      | 
+|German  | de |Indonesian          | id  | Norwegian        | no | Turkish                   | tr                   | 
+|English | en |Italian             | it  | Polish           | pl  | Ukrainian                 | uk           | 
+|Spanish | es |Japanese            | ja  | Portuguese       | pt   | Vietnamese                | vi                   | 
+|Finnish | fi |Korean              | ko  | Romanian         | ro                  | Chinese                   | zh  | 
+
+## Model Downloads
+| Model Name  | Description | Download |
+| ----------- | ----------- |-----------
+| 👉 **Seed-X-Instruct**  | Instruction-tuned for alignment with user intent. |🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-X-Instruct-7B)|
+| Seed-X-PPO | RL trained to boost translation capabilities.     | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-X-PPO-7B)|
+|Seed-X-RM | Reward model to evaluate the quality of translation.|  🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-X-RM-7B)| 
+
+## Quickstart
+Here is a simple example demonstrating how to load the model and perform translation using ```vllm```
+```python
+from vllm import LLM, SamplingParams
+
+model_path = "./ByteDance-Seed/Seed-X-Instruct-7B"
+
+model = LLM(model=model_path,
+            max_num_seqs=512,
+            tensor_parallel_size=8,
+            enable_prefix_caching=True, 
+            gpu_memory_utilization=0.95)
+
+messages = [
+    "Translate the following English sentence into Chinese:\nMay the force be with you <zh>", # without CoT
+    "Translate the following English sentence into Chinese and explain it in detail:\nMay the force be with you <zh>" # with CoT
+]
+
+sampling_params = SamplingParams(temperature=0,
+                                 max_tokens=4096,
+                                 skip_special_tokens=True)
+
+results = model.generate(messages, sampling_params)
+responses = [res.outputs[0].text.strip() for res in results]
+
+print(responses)
+```
+## Evaluation
+We evaluated Seed-X on a diverse set of translation benchmarks, including FLORES-200, WMT-25, and a publicly released [challenge set](https://github.com/ByteDance-Seed/Seed-X-7B/tree/main/challenge_set) accompanied by human evaluations.
+![humen_eval](imgs/humen_eval.png)
+For detailed benchmark results and analysis, please refer to our [Technical Report](https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/Technical_Report.pdf).
+
+## License
+This project is licensed under OpenMDW. See the [LICENSE](https://github.com/ByteDance-Seed/Seed-X-7B/blob/main/LICENSE.openmdw) flie for details.
+
+## Citation
+If you find Seed-X useful for your research and applications, feel free to give us a star ⭐ or cite us using:
+```bibtex
+@Article{XXX,
+      title={XXXXXXXXXXX}, 
+      author={XXX,XXX,XXX,XXX},
+      year={2025},
+      eprint={XXXX.XXXXX},
+      archivePrefix={arXiv},
+      primaryClass={cs.XX}
+}
+```
+We will soon publish our technical report on Arxiv.