Build an LLM from Scratch 6: Finetuning for Classification

Links to the book: - https://amzn.to/4fqvn0D (Amazon) - https://mng.bz/M96o (Manning) Link to the GitHub repository: https://github.com/rasbt/LLMs-from-scratch This is a supplementary video explaining how to finetune an LLM as a classifier (here using a spam classification example) as a gentle introduction to fine-tuning, before instruction finetuning the LLM in the next video. 00:00 6.2 Preparing the dataset 26:49 6.3 Creating data loaders 42:50 6.4 Initializing a model with pretrained weights 52:56 6.5 Adding a classification head 1:08:28 6.6 Calculating the classification loss and accuracy 1:30:54 6.7 Finetuning the model on supervised data 2:04:25 6.8 Using the LLM as a spam classifier You can find additional bonus materials on GitHub: Additional experiments finetuning different layers and using larger models, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch06/02_bonus_additional-experiments Finetuning different models on 50k IMDB movie review dataset, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch06/03_bonus_imdb-classification Building a User Interface to Interact With the GPT-based Spam Classifier, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch06/04_user_interface