FASTER Code for Supervised Fine-Tuning and DPO Training with UNSLOTH

Learn to accelerate Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) training for Large Language Models through a detailed video tutorial that explores two free Jupyter notebooks. Dive into practical implementations using HuggingFace-compatible scripts for training LLama or Mistral models, with step-by-step demonstrations of the free version's capabilities. Access comprehensive examples including Alpaca with Mistral 7b implementation and DPO Zephyr training, complete with direct links to ready-to-use Google Colab notebooks for hands-on experimentation in AI model training and optimization.