Update README.md

README.md

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 ## Description
-**Ruadapt** is a project focused on developing the methodology for adapting large language models (LLMs) to the Russian language, with a change in tokenization to enhance model efficiency.
+**Ruadapt** is a project focused on developing the methodology for adapting large language models (LLMs) to the Russian language, with a change in tokenization to enhance model efficiency. It is important to note that the methodology is **applicable to practically any language**, as it does not employ any language-dependent methods.
 
 In addition to developing the methodology itself, we also employ it to adapt existing SOTA open-source models and make them publicly available. For example, our series of RaadaptQwen2.5 models generate Russian-language text 30-60% faster (in terms of characters) due to more suitable tokenization, while minimizing quality loss on both English and Russian languages.
 
@@ -16,7 +16,7 @@ One of the unique features of our approach to adaptation lies in the fact that,
 
 An intriguing aspect of adapting T-pro-it-1.0 is that this model was obtained through continuous pretraining on over 100 billion tokens of Russian-language data using full fine-tuning. Despite this extensive prior training, our methodology still worked effectively (note: the original base model Qwen2.5-32B was adapted!), and the resulting adapted version either outperformed or matched T-pro-it-1.0 on several benchmarks. Moreover, it demonstrated higher efficiency in Russian-language tokenization.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/652cedbdf120598322ae358a/L-jQw1MjhdAUbkqVfcrt-.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/652cedbdf120598322ae358a/L-jQw1MjhdAUbkqVfcrt-.png){ width=50% }
 
 ## Papers
 Tikhomirov M., Chernyshov D. Facilitating Large Language Model Russian Adaptation with Learned Embedding Propagation //Journal of Language and Education. – 2024. – Т. 10. – №. 4. – С. 130-145. (Preprint: https://arxiv.org/abs/2412.21140)