video1.0<iframe src="https://www.loom.com/embed/357d30b71c0340689527599db7c0e939" frameborder="0" width="1260" height="945" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9451260Loomhttps://www.loom.com9451260https://cdn.loom.com/sessions/thumbnails/357d30b71c0340689527599db7c0e939-b730887a9ea32306.gif753.992In this final part of my video series, I introduce little GPT, my ultimate deliverable that showcases the application of the math and neural networks we've discussed. I explain the importance of device preference, model precision, and how our model predicts responses based on training data. I also demonstrate retrieval augmented generation (RAG), which enhances the model's ability to respond by incorporating uploaded files for context. While the model has limitations due to its size and training data, I encourage you to experiment with it and upload your own files to see the benefits firsthand. Please subscribe for more updates and improvements as I continue to refine this project.