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).

There are various kind of PEFT methods but in this article we will stick with Adapter-like methods like LoRa

PEFT approaches enable you to get performance comparable to full fine-tuning while only having a small number of trainable parameters. You wonder how…