RUGPT3MEDIUM_BASED_ON_GPT2

Introduction

RUGPT3MEDIUM_BASED_ON_GPT2 is a language model designed for text generation tasks in Russian. It employs a transformer architecture, specifically leveraging the capabilities of GPT-2, and is implemented using the PyTorch framework. This model was developed to improve the performance and understanding of Russian language tasks.

Architecture

The model is based on the GPT-2 architecture and was pretrained with a sequence length of 1024 tokens. Post pretraining, the model underwent finetuning with a context size of 2048 tokens. The training utilized the Transformers library.

Training

The training process involved using 80 billion tokens over 3 epochs. The entire training procedure spanned approximately 16 days on 64 GPUs. The model achieved a perplexity of 17.4 on the test set, signifying its proficiency in handling Russian language tasks.

Guide: Running Locally

To run RUGPT3MEDIUM_BASED_ON_GPT2 locally, follow these steps:

1. Install Dependencies: Ensure that the Transformers library and PyTorch are installed in your Python environment.
2. Download the Model: Obtain the model from the Hugging Face Model Hub.
3. Load the Model: Use the Transformers library to load the model and tokenizer.
4. Inference: Input text to generate predictions or complete text sequences.

For more efficient performance, particularly during training, consider using cloud GPUs such as those offered by AWS, Google Cloud, or Azure.

License

The model's code and pre-trained weights are available for use under the terms specified by the creators. Be sure to review any accompanying documentation for specific licensing details or restrictions.