Fine-Tuning with Finesse: QLoRA
Elevating Intelligence of Llama with LoRA
This article serves as an extension to Fine-Tuning with Finesse: Parameter Efficient Fine-Tuning (PEFT) offering a hands-on exploration of one of the PEFT method i.e. QLoRA through practical code implementation. Lets begin 🔰
As we know , Large Language Models (LLMs) based on the transformer architecture, like GPT, T5, and BERT have consistently demonstrated top-tier performance across a range of Natural Language Processing (NLP) tasks.
The traditional approach involves initially extensive pretraining on vast datasets, followed by fine-tuning for specific downstream tasks. Fine-tuning pretrained LLMs on these downstream datasets significantly enhances performance compared to using the pretrained LLMs directly.
However, fine-tuning LLMs presents numerous difficulties, as outlined in the “Challenges with Standard Fine-tuning” section of the Fine-Tuning with Finesse: Parameter Efficient Fine-Tuning (PEFT).
PEFT approaches enable you to get performance comparable to full fine-tuning while only having a small number of trainable parameters. You wonder how…