Fine-tuning the Llama 2 Model with Minimal VRAM
A Comprehensive Guide for Optimizing Results
Introduction
In the realm of artificial intelligence, the advent of large language models (LLMs) has revolutionized various domains. Among these LLMs, Llama 2 stands out with its impressive 7 billion parameters. However, fine-tuning such models can be resource-intensive, requiring powerful GPUs with ample video memory (VRAM).
This article aims to provide a comprehensive guide on how to fine-tune the Llama 2 model with minimal VRAM, making it accessible to a wider range of users with limited hardware resources. We will cover essential steps, parameter optimization, and troubleshooting techniques to ensure successful execution.
Comments