Build an LLM from Scratch 7: Instruction Finetuning

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 instruction finetune an LLM. 00:00 7.2 Preparing a dataset for supervised instruction finetuning 15:37 7.3 Organizing data into training batches 39:17 7.4 Creating data loaders for an instruction dataset 46:44 7.5 Loading a pretrained LLM 54:25 7.6 Finetuning the LLM on instruction data 1:14:20 7.7 Extracting and saving responses 1:23:56 7.8 Evaluating the finetuned LLM You can find additional bonus materials on GitHub Generating a Dataset for Instruction Finetuning, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch07/03_model-evaluation Direct Preference Optimization (DPO) for LLM Alignment, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch07/04_preference-tuning-with-dpo Building a User Interface to Interact With the Instruction Finetuned GPT Model, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch07/06_user_interface Evaluating Instruction Responses Using the OpenAI API and Ollama, https://github.com/rasbt/LLMs-from-scratch/tree/main/ch07/03_model-evaluation