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RefalMachine commited on Mar 22

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@@ -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/H_IUJ1CGgSI7keXJ6ZMUr.png)
 ## 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)

 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/3hfEDUakmRV9H6vFFaV1l.png)
 ## 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)