Update README.md

README.md

@@ -15,11 +15,9 @@ pipeline_tag: translation
 
 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 model version of GemmaX2-28-2B-Pretrain after SFT.
-
 - **Developed by:** Xiaomi
-- **Model type:** A 2B parameter model base on Gemma2, we obtained GemmaX2-28-2B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data. Afterward, GemmaX2-28-2B-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. 
+- **Model type:** A 2B parameter model base on Gemma2-2B, we obtained GemmaX2-28-2B-Pretrain by continuing pre-training on a large amount of monolingual and parallel data.
+- **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. 
 - **License:**  gemma
 
 ### Model Source
@@ -30,28 +28,6 @@ GemmaX2-28-2B-v0.1 is the model version of GemmaX2-28-2B-Pretrain after SFT.
 
 ![Experimental Result](main.png)
 
-## 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 GemmaX2-28-2B's translation performance, and future models will be release in due course.
-
-
-
-## Run the model
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "ModelSpace/GemmaX2-28-2B-Pretrain"
-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