Trevor Chow’s Post

How LLMs Work in 3 Sentences: 1) During training, LLMs digest sequential inputs of text data, which are converted into numerical representations called embeddings. 2) These embeddings capture the semantic meaning and contextual relationships between words, facilitating the model's understanding of language. 3) Through iterative adjustments of internal parameters via back-propagation, LLMs fine-tune their ability to predict the next word or sequence of words in a given context. Just as a chef combines ingredients like a bun, a burger, and cheese to make a cheeseburger, a large language model turns your words into embeddings, mixing them to generate useful responses.

How LLMs Work in 3 Sentences: 1) During training, LLMs digest sequential inputs of text data, which are converted into numerical representations called embeddings. 2) These embeddings capture the semantic meaning and contextual relationships between words, facilitating the model's understanding of language. 3) Through iterative adjustments of internal parameters via back-propagation, LLMs fine-tune their ability to predict the next word or sequence of words in a given context. Just as a chef combines ingredients like a bun, a burger, and cheese to make a cheeseburger, a large language model turns your words into embeddings, mixing them to generate useful responses.

Advanced Recipes for Contrastive Learning via #TowardsAI → https://bit.ly/3NbafQ5

How LLMs Work in 3 Sentences: 1) During training, LLMs digest sequential inputs of text data, which are converted into numerical representations called embeddings. 2) These embeddings capture the semantic meaning and contextual relationships between words, facilitating the model's understanding of language. 3) Through iterative adjustments of internal parameters via back-propagation, LLMs fine-tune their ability to predict the next word or sequence of words in a given context. Just as a chef combines ingredients like a bun, a burger, and cheese to make a cheeseburger, a large language model turns your words into embeddings, mixing them to generate useful responses.

This week I want to share an excellent blog post by someone else: This blogpost explores in a very nice and intuitive way how to run distributed training in JAX. https://lnkd.in/ebkZR9sn

🚀 Demystifying DDP & FSDP for AI Developers 🚀 As AI models grow in scale, techniques like Data Parallelism (DDP) and Fully Sharded Data Parallel (FSDP) have become essential tools for efficient distributed training. But understanding how these techniques work can be a challenge, especially when diving into the theoretical concepts behind them. In my latest Medium article, I break down these advanced concepts in a simple yet theoretical way, aiming to make them accessible to developers at all levels. If you’ve ever felt overwhelmed by DDP and FSDP or struggled to grasp their inner workings, this article is for you! 🔍 What’s Inside? 1. A clear, step-by-step explanation of Data Parallelism and Fully Sharded Data Parallel. 2. How these methods optimize training and scale models effectively. 3. Insights into the underlying theory, presented in an intuitive way. Whether you're an AI researcher, developer, or just someone interested in the mechanics behind large-scale deep learning, I hope this article helps you navigate the complexity of DDP and FSDP with confidence! #MachineLearning #DeepLearning #DistributedTraining #DDP #FSDP #TechExplained #ModelTraining #LLMs

How training LLMs have changed focus from just parameters, to balancing parameters and data, to now which is prima focus on data.

Building a search application has never been so easy 👇 Marqo has got you covered from training, to inference, to storage. You don't need to calculate the vectors yourself, simply select the model you want to use. Marqo supports hundreds of embedding models out of the box, as well as custom weights, and models fine-tuned with Marqtune (Marqo's Generalized Contrastive Learning framework). Find out more: https://lnkd.in/dKRiyzqs

The most common goal that my search clients express is a desire to improve their ranking. This post sketches out some challenges of obtaining labeled training data to implement machine-learned ranking, aka learning to rank (LTR).

Our recent study found that user training is a game-changer when it comes to AI-infused products. Learn more by downloading the full report! bit.ly/sparqaistudy