Finetuning allows us to adapt pretrained LLMs in a cost-efficient manner. But which method should we use? This article compares different…
Posted by Terence Parr, Google
Decision trees are the fundamental building block of Gradient Boosted Trees and Random Forests, the two most popular machine learning models for tabular data. To learn how decision trees work and how to interpret …
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…
The stark success of OpenAI’s GPT4 model surprised me shifting my view from “really good autocomplete” (roughly inline with intuitions here) to a dialog agent exhibiting a significant scope of reasoning and intelligence. Some of the MSR folks did a fairly thorough study of capabilities which seems like a good reference. I think of GPT4 …
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…