Update README.md

README.md

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-license: other
-license_name: license
-license_link: LICENSE
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+---
+license: other
+license_name: license
+license_link: LICENSE
+---
+
+# Model Card for GemmaX2-28
+
+## Model Details
+
+### Model Description
+
+GemmaX2-28-2B-Pretrain is a language model that results from continual pretraining of Gemma2-2B on a mix of 56 billion tokens of monolingual and parallel data in 28 different languages — Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese. 
+
+GemmaX2-28-2B-v0.1 is the first model in the series. Compared to the current open-source state-of-the-art (SOTA) models, it achieves optimal translation performance across 28 languages, even reaching performance comparable to GPT-4 and Google Translate, indicating it has achieved translation capabilities on par with industry standards. 
+
+- **Developed by:** Xiaomi
+- **Model type:** A 2B parameter model base on Gemma2, we obtained GemmaX2-28-9B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data. Afterward, GemmaX2-28-9B-v0.1 was derived through supervised fine-tuning on a small set of high-quality instruction data.
+- **Language(s) (NLP):** Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese. 
+- **License:**  gemma
+
+### Model Source
+
+- paper: coming soon.
+
+### Model Performance
+
+![Experimental Result](main.jpg)
+
+## Limitations
+
+GemmaX2-28-2B-v0.1 supports only the 28 most commonly used languages and does not guarantee powerful translation performance for other languages. Additionally, we will continue to improve GemmaX2-28-9B's translation performance, and future models will be release in due course.
+
+
+
+## Run the model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ModelMagician/GemmaX2-28-9B-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
+inputs = tokenizer(text, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Training Data
+
+We collected monolingual data from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). For parallel data, we collected all Chinese-centric and English-centric parallel dataset from the [OPUS](https://opus.nlpl.eu/) collection up to Auguest 2024 and underwent a series of filtering processes, such as language detection, semantic duplication filtering, quality filtering, and more.
+
+## Citation 
+
+```bibtex
+@misc{gemmax2,
+    title = {Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study},
+    url = {},
+    author = {XiaoMi Team},
+    month = {October},
+    year = {2024}
+}
+```