In this video you’ll learn how to:
• What fine-tuning is, how it compares to RAG (Retrieval-Augmented Generation), and when to use each
• Coding a PyTorch fine-tuning script for small models
• Formatting custom training data for LLMs
• Running fine-tuning jobs in a Docker container
• Merging fine-tuned adapters into a single model
•Quantizing the model to run efficiently on edge or local devices
• Testing your fine-tuned model on a desktop with LM Studio
If you’re a developer or AI engineer who wants to get hands-on with customizing small language models, this deep-dive is for you.
Link to Github repo with scripts and data used in the video:
Link to NVIDIA Spark DGX Training with PyTorch Playbook:
