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Explore and Implement QLoRA for Efficient Quantization and Adapter-Based Finetuning
Learn State-of-Art LLM Finetuning Strategy, QLoRA, with Reduced Computational Resources.
In this post, you will learn what QLoRA is, what features of QLoRA make it efficient, and how to finetune a quantized Large Language Model (LLM) using QLoRA.
I recommend reading the articles below for a comprehensive overview of the Large Language Model finetuning techniques.
An Intuitive Guide to Finetuning Large Language Model
A Simplified Guide to LoRA for Large Language Models
What is Finetuning LLM?
Fine-tuning is a method to enhance the capabilities of a pre trained Large Language Models (LLMs) to learn new, domain-specific knowledge and improving their performance for a specific task.
LLMs are pre-trained on massive amounts of text; finetuning takes a pre-trained LLM and focuses its learning on a specific task or domain, which is expensive and resource-intensive; however, quantization methods can reduce the memory footprint of LLMs, making finetuning efficient and cost-effective.
