Building LLMs from scratch - Coding a fine-tuned LLM spam classification model | From Scratch

In this lecture, we completely implement a finetuned GPT-based spam classification model. We perform the fine-tuning training and testing completely from scratch and build our own LLM fine-tuned classifier! This is a dense 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 Recap of classification fine-tuning so far 5:51 Converting LLM outputs to predicted labels 9:34 Measuring classification accuracy 15:23 Cross entropy loss function implementation 24:04 Fine-tuning training loop implementation 33:35 Analysing training results 38:36 Testing model on new data 45:22 Next steps for exploration and research Code file: https://drive.google.com/file/d/1oXbv0T9t74pEXejQ3o7w9ZABGLjHmFsu/view?usp=sharing Spam vs No-Spam dataset: https://archive.ics.uci.edu/dataset/228/sms+spam+collection PyTorch cross entropy implementation: 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 PyTorch saving model parameters: https://pytorch.org/docs/stable/generated/torch.save.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/