
# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).


The model was pretrained with sequence length 1024 using the Transformers library by the [SberDevices](https://sberdevices.ru/) team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.

Total training time was around 16 days on 64 GPUs.  
The final perplexity on the test set is `17.4`.

# Authors
+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
  + Dmitry Zmitrovich 

# Cite us
```
@misc{zmitrovich2023family,
      title={A Family of Pretrained Transformer Language Models for Russian}, 
      author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
      year={2023},
      eprint={2309.10931},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

{"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"}
transformers pytorch gpt2 text-generation PyTorch Transformers ru arxiv:2309.10931 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.


The model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.

Total training time was around 16 days on 64 GPUs.  
The final perplexity on the test set is '17.4'.

# Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich 

# Cite us


# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.


The model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.

Total training time was around 16 days on 64 GPUs.  
The final perplexity on the test set is '17.4'. # Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich # Cite us
TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.


The model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.

Total training time was around 16 days on 64 GPUs.  
The final perplexity on the test set is '17.4'. # Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich # Cite us
passage: TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.


The model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens for 3 epochs. After that, the model was finetuned with the context size of 2048 tokens.

Total training time was around 16 days on 64 GPUs.  
The final perplexity on the test set is '17.4'.# Authors
+ NLP core team RnD Telegram channel:
  + Dmitry Zmitrovich# Cite us