Researchers at Hugging Face have developed a new technique to improve the efficiency of training large language models. By packing model weights into smaller chunks, they were able to reduce memory usage and speed up training times using Flash Attention 2 (FA2). This approach can be applied to various transformer-based architectures, including those used in popular Hugging Face libraries. --- Why it matters: This matters because efficient training of large language models is crucial for their widespread adoption. Faster and more cost-effective training enables researchers to experiment with larger models and more complex tasks, driving progress in natural language processing and related fields. Source: https://huggingface.co/blog/packing-with-FA2