Building LLMs from scratch - Coding the entire LLM Pre-training Loop

In this lecture, we code the LLM pretraining loop and run LLM pretraining on a hands on book dataset! We plot the training and validation losses and analyze the LLM performance We understand the theory, mathematical intuition and also do the coding for the entire implementation. The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book! This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations. Affiliate links for purchasing the book will be added soon. Stay tuned for updates! 0:00 LLM loss function recap 10:23 LLM Pretraining loop 17:05 Measuring LLM parameters (~160 M) 21:38 Coding the LLM Pretraining loop 30:10 Pretraining the LLM on our dataset 34:15 Analyzing pretraining results 39:20 LLM Overfitting 40:56 Next steps Link to code file: https://drive.google.com/file/d/1p87nemE6wFjGhkX4kC7KfGv1juMZJrMr/view?usp=sharing Link to dataset: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch02/01_main-chapter-code/the-verdict.txt PyTorch Datasets and DataLoaders: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html PyTorch Cross Entropy: https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html PyTorch ADAMW optimizer: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html ================================================= ✉️ Join our FREE Newsletter: https://vizuara.ai/our-newsletter/ ================================================= Vizuara philosophy: As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there. Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch. No cost. No hidden charges. Pure old school teaching and learning. ================================================= 🌟 Meet Our Team: 🌟 🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper) 🔗 LinkedIn: https://www.linkedin.com/in/raj-abhijit-dandekar-67a33118a/ 🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/rajat-dandekar-901324b1/ 🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/sreedath-panat-8a03b69a/ 🎓 Sahil Pocker (Machine Learning Engineer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/sahil-p-a7a30a8b/ 🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23) 🔗 LinkedIn: https://www.linkedin.com/in/abhijeet-singh-9a1881192/ 🎓 Sourav Jana (Software Developer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/souravjana131/