Working in AI Workspaces: Hands-On Tutorial#
This hands-on tutorial demonstrates how to effectively work within AMD AI Workbench workspaces using JupyterLab notebooks. You’ll learn how to fine-tune large language models, and leverage the integrated development environment.
Prerequisites#
Access to AMD AI Workbench with an active JupyterLab workspace
Basic familiarity with Python and Jupyter notebooks
A Hugging Face account (for dataset access)
For instructions on launching JupyterLab in AI Workbench, see how to deploy and run inference.
Fine-Tuning Llama-3.1 8B with LLaMa-Factory#
This tutorial guides you through fine-tuning the Llama-3.1 8B large language model (LLM) on AMD ROCm GPUs using LLaMa-Factory. LLaMa-Factory is a user-friendly, unified framework designed for training and fine-tuning large language models with minimal setup requirements.
The complete fine-tuning tutorial is available in the ROCm AI Tutorials.
Tip
You can jump directly to step 4 in the “Prepare the training environment” section. AI Workbench provides JupyterLab notebooks out of the box, so no additional environment setup is required.