Fine-tuning LLMs with Huggingface and PyTorch - A Step-by-Step Tutorial

Learn to fine-tune Large Language Models (LLMs) locally through a comprehensive step-by-step tutorial that demonstrates using Meta's Llama-3.2-1B-Instruct model with Huggingface Transformers and PyTorch. Master essential concepts including prompting techniques, dataset creation, input-output pair generation, loss functions, PyTorch optimizers, and PEFT LORA adapters while working towards paper category prediction. Explore fundamental components like tokenizers, instruction prompts, chat templates, and next word prediction, culminating in both complete fine-tuning with PyTorch and LORA fine-tuning with PEFT. Designed for MacBook Pro M2 users with 16GB RAM, the tutorial provides practical insights for local machine learning, though notes on hardware limitations and quantization considerations for NVIDIA GPUs are included. Access accompanying code, notebooks, datasets, slides, animations, and detailed write-ups through the creator's Patreon platform.