Fine-Tuning Local Models with LoRA in Python LoRA (Low-Rank Adaptation) is a technique for fine-tuning large language models by injecting trainable low-rank matrices into each model layer, allowing adaptation with significantly fewer parameters. This makes training more efficient, memory-friendly (and mainly cheaper) while preserving the original model weights. This one-hour tutorial, by NeuralNine, focuses on the theoretical and practical approach for fine-tuning LLMs with LoRA using Python, and it covers: ✅ Theory & Mathematics ✅ Fine-Tuning on Math Problems ✅ Evaluation of Math Problems ✅ Fine-Tuning on Custom Data ✅ Evaluation of Custom Data https://www.youtube.com/watch?v=XDOSVh9jJiA