#pre-training — 1sec.ai

Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models

Researchers from Hugging Face and collaborators propose Cosmopedia, a method for generating large-scale synthetic data to pre-train Large Language Models. This approach uses a combination of LLMs and text-to-text models to create diverse, high-quality training data. The generated data can help improve model performance, especially in low-resource languages. You can explore the generated dataset and code on the Hugging Face Hub.

Key takeaways

Cosmopedia generates synthetic data using LLMs and text-to-text models.
The approach aims to improve model performance, especially in low-resource languages.
The dataset and code are available on the Hugging Face Hub.

HHugging Face Blog#synthetic-data #pre-training #large-language-models

modelsAug 22

Pre-Train BERT with Hugging Face Transformers and Habana Gaudi

You can pre-train BERT using Hugging Face Transformers and Habana Gaudi, a hardware accelerator designed for large-scale deep learning workloads. This combination enables efficient and scalable pre-training of BERT models. Builders can leverage this setup for their own pre-training tasks. The integration supports large-scale model training.

Key takeaways

Hugging Face Transformers supports pre-training BERT on Habana Gaudi.
Gaudi is a hardware accelerator for large-scale deep learning.
This integration enables efficient pre-training of BERT models.

HHugging Face Blog#pre-training #hardware-accelerator #transformers