Company Joins International Community of AI Organizations, Led by IBM and Meta, That Includes Leading Startups, Academia, Research and Government Organizations Lightning AI, the company behind PyTorch Lightning, today announced it has joined the AI Alliance, which includes more than 50 leading organizations across industries that are coming together to support open AI research, development,... Read more »| Lightning AI
In this article, we will work with a vision transformer from PyTorch’s Torchvision library, providing simple code examples that you can execute on your own machine without the need to download and install numerous code and dataset dependencies. The self-contained baseline training script comprises approximately 100 lines of code, excluding whitespace and code comments.... Read more »| Lightning AI
Introduction The aim of 8-bit quantization is to reduce the memory usage of the model parameters by using lower precision types than full (float32) or half (bfloat16) precision. Meaning – 8-bit quantization compresses models that have billions of parameters like Llama 2 or SDXL and makes them require less memory. Thankfully, Lightning Fabric makes quantization... Read more »| Lightning AI
Introduction The aim of 4-bit quantization is to reduce the memory usage of the model parameters by using lower precision types than full (float32) or half (bfloat16) precision. Meaning – 4-bit quantization compresses models that have billions of parameters like Llama 2 or SDXL and makes them require less memory. Thankfully, Lightning Fabric makes quantization... Read more »| Lightning AI
Introduction Some engineers may prefer the greatest amount of control as possible over their PyTorch training loop. Yet, those same engineers also know the benefits of a lightweight, production ready framework that handles the heavy lifting when it comes to SOTA distributed training features. The Power of Lightning Fabric Lightning Fabric does this heavy lifting... Read more »| Lightning AI
In this blog, you will learn about the different components of PyTorch Lightning and how to train an image classifier on the CIFAR-10 dataset with PyTorch Lightning. We will also discuss how to use loggers and callbacks like Tensorboard, ModelCheckpoint, etc. PyTorch Lightning is a high-level wrapper over PyTorch which makes model training easier and... Read more »| Lightning AI
The code in this tutorial is available on GitHub in the text-lab repo. Clone the repo and follow along! Introduction Training deep learning models at scale is an incredibly interesting and complex task. Reproducibility for projects is key, and reproducible code bases are exactly what we get when we leverage PyTorch Lightning for training and... Read more »| Lightning AI
If you’ve been working with PyTorch, you’re likely familiar with its power and flexibility in building and training deep learning models. However, as your projects become more complex and your codebase grows, you may find yourself spending a significant amount of time on boilerplate code for managing training loops, handling data loaders, and implementing common... Read more »| Lightning AI
LoRA is one of the most widely used, parameter-efficient finetuning techniques for training custom LLMs. From saving memory with QLoRA to selecting the optimal LoRA settings, this article provides practical insights for those interested in applying it.| Lightning AI