Now that we’ve trained, evaluated, and fine-tuned the Transformer model, the next step is to deploy the model so it can be used in real time applications. In this post, we’ll cover: By the end of this post, you’ll be able to deploy a Transformer model and interact with it Read more…
In the previous post, we discussed training the Transformer model, monitoring loss, and ensuring the model improves over time. Now, it’s time to take a step forward: evaluating the model’s performance and fine tuning it to make it more efficient and accurate. In this post, we’ll focus on: Evaluating the Read more…
In the previous two posts, we built the basic and scalable versions of the Transformer model. Now, it’s time to move on to the next critical step of training the model. In this post, we’ll focus on: By the end of this post, you’ll have a Transformer model that can Read more…
In the previous post, we learned about the basics of Transformer models and how they’re used to process language. Now, let’s go a bit deeper and look at how to build each important part of a Transformer. By the end of this post, we’ll have a full, working Transformer model Read more…
In 2017, the AI landscape for language understanding changed dramatically when Google Brain researchers introduced a groundbreaking algorithm called the “Transformer” in their paper “Attention is All You Need.” Before this, AI models could only process one word at a time, often resulting in awkward, incomplete language. The Transformer’s self-attention Read more…
Nvidia just dropped their Nemotron 70b model, and it’s blowing past competitors like OpenAI’s GPT-4o and Anthropic’s Claude-3. It’s completely open-source and free, making it a game changer for developers and researchers. If you’ve been keeping an eye on the AI landscape, this is a pivotal moment. The Llama-3.1-Nemotron-70B-Instruct model Read more…