Large language models (LLMs) offer one of the most interesting opportunities for developing more efficient training methods. A few weeks ago, the NeurIPS…
Peak memory consumption is a common bottleneck when training deep learning models such as vision transformers and LLMs. This article provides a series of…
Finetuning allows us to adapt pretrained LLMs in a cost-efficient manner. But which method should we use? This article compares different…
Training and using large language models (LLMs) is expensive due to their large compute requirements and memory footprints. This article will explore how…
Pretrained large language models are often referred to as foundation models for a good reason: they perform well on various tasks, and we can use them as a…
In the rapidly evolving field of artificial intelligence, utilizing large language models in an efficient and effective manner has become increasingly…
Previously, I shared an article using multi-GPU training strategies to speed up the finetuning of large language models. Several of these strategies include…
When it comes to productivity workflows, there are a lot of things I’d love to share. However, the one topic many people ask me about is how I keep up with…
This blog post outlines techniques for improving the training performance of your PyTorch model without compromising its accuracy. To do so, we will wrap a…
In this article, we are going to understand how self-attention works from scratch. This means we will code it ourselves one step at a time. Since its…