Building LLMs from scratch - LLM fine-tuning training loop | Coded from scratch

In this lecture, we run the LLM fine-tuning loop on the instruction dataset. We demonstrate how fine-tuning can improve LLM performance while following instructions. This is a lecture explained through detailed whiteboard notes and live coding. 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 Instruction finetuning process 5:13 Understanding the finetuning training loop 6:22 Understanding the finetuning loss function 9:20 Coding the finetuning training loop in Python 17:22 Monitoring the finetuned LLM results 22:57 Recap and next steps Code file: https://drive.google.com/file/d/1OCpr4GZVmNNEXDpLuG2GZ1LlnJ_uvqPN/view?usp=sharing Instruction data link: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/instruction-data.json Stanford Alpaca link: https://github.com/tatsu-lab/stanford_alpaca PyTorch cross entropy loss: https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html PyTorch Datasets and Dataloaders: https://pytorch.org/tutorials/beginner/basics/data_tutorial.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/