Ru-Gpts

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ru-gpts is an open-source AI tool that provides Russian GPT-3 and GPT-2 models. It offers various sizes of autoregressive transformer language models trained on a large Russian language dataset.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ru-gpts?

ru-gpts is a collection of open-source Russian GPT-3 models, including ruGPT3XL, ruGPT3Large, ruGPT3Medium, and ruGPT3Small, alongside a ruGPT2Large model. These autoregressive transformer language models are trained on an extensive dataset of the Russian language, offering different sequence lengths and attention mechanisms. The repository provides detailed setup instructions for both Colab and local environments, usage examples for generation, and finetuning capabilities. Pretraining details for each model, including dataset size, training time, and hardware used, are also available. The project is developed by AI-Forever and Sberbank-AI, making advanced Russian language models accessible for various applications.

Best used for

Ideal for AI researchers and developers who need to implement advanced Russian language models, finetune them for specific tasks, and explore their capabilities. Especially valuable for projects requiring text generation, simplification, detoxification, or paraphrasing in the Russian language.

Common actions

generate text

finetune models

research language models

develop AI applications

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Capabilities

Key features

Russian GPT-3 models
Russian GPT-2 model
Autoregressive transformers
Sparse attention blocks
Finetuning capabilities
HuggingFace integration

Target Audience

professor

Integrations

huggingface

Pricing & Plans

Open Source

Free

FAQs

What specific Russian GPT models are available in ru-gpts?

The ru-gpts repository includes ruGPT3XL, ruGPT3Large, ruGPT3Medium, ruGPT3Small, and ruGPT2Large models. These models vary in size, sequence length, and attention mechanisms, offering a range of options for different applications.

What are the hardware requirements for using ruGPT3XL?

For ruGPT3XL, the setup instructions recommend specific installations including clang-9, llvm-9, apex, triton, and deepspeed. The model was originally trained on 256 GPUs, indicating it's resource-intensive, though usage examples might run on less.

Can I finetune these models for my specific use case?

Yes, the repository provides examples and scripts for finetuning the ru-gpts models. This allows users to adapt the pre-trained models to their particular datasets and tasks, enhancing their performance for specialized applications.

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Also listed in

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Research & Education › Academic Research Content & Design › AI Writing Assistants Content & Design › Translation & Localization

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