Multilingual GPT model

We introduce a family of autoregressive GPT-like models with 1.3 billion parameters trained on 60 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, Deepspeed and Megatron frameworks allows us to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released XGLM models at the same time covering more languages and enhancing NLP possibilities for low resource languages.