LLAMA-3.1: EASIET WAY To FINE-TUNE ON YOUR DATA

In this detailed tutorial, the host from Prompt Engineering demonstrates how to efficiently fine-tune the Llama 3.1 model using advanced techniques like Unsloth, LoRa, and QLoRa. The video begins by introducing the significance of open-weight models and their ability to close the performance gap with larger closed-source models. The host outlines the different stages of model training, including pre-training, supervised fine-tuning, and preference alignment, explaining how each stage contributes to the model’s performance. Viewers are guided through the installation of required packages and the setup of a training environment using Google Colab. The tutorial emphasizes the importance of data preparation and the prompt template necessary for fine-tuning. The host explores the benefits of using LoRa and QLoRa, highlighting their ability to reduce VRAM requirements while maintaining performance. Throughout the video, practical examples illustrate the fine-tuning process, including how to save and load models for future use. The tutorial concludes with an invitation for viewers to experiment with the Llama 3.1 model and explore its potential applications.