Training and Inference of LLMs with PyTorch Fully Sharded Data Parallel and Better Transformer
In this blog we show how to perform efficient and optimized distributed training and inference of large language models using PyTorch’s Fully Sharded Data Parallel and Better Transformer implementations, on the Spark platform. In this implementation, we combine Microsoft Fabric for data preparation and model inference, and Azure Databricks for model training, having all our data under Microsoft Fabric’s OneLake. The code for this blog is available at this GitHub repository, as a series of PySpark notebooks for Microsoft Fabric and Azure Databricks.Interview with Jeremy Howard Fast.ai AI Application without a PhD
Fast.ai has made it their mission to make deep learning as accessible as possible, and in this interview fast.ai co-founder Jeremy Howard explains how to use their free software and courses to become an effective deep learning practitioner.Responsible Synthetic Data Creation for Fine-Tuning with RAFT Distillation
This blog will explore the process of crafting responsible synthetic data, evaluating it, and using it for fine-tuning models. We’ll also dive into Azure AI’s RAFT distillation recipe, a novel approach to generating synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.
