Finetuning LLM with QLoRa on financial data for sentiment classification

Continuing from my previous tutorial on using RAG to enhance LLM models with more recent or domain-specific knowledge, you can apply this approach to query information within your own documents or website. However, the fundamental LLM model has limitations when providing opinions on specific topics unrelated to document retrieval, such as sentiment classification. In such cases, fine-tuning the LLM becomes essential to tailor the model to your specific use case. This blog post will guide you through the process of fine-tuning an LLM model on limited hardware, such as a basic T4 in Colab, which has 16GB GPU memory.

In light of the evolving landscape of finance, where sentiment analysis has become a vital tool for understanding market trends and making well-informed decisions, the fine-tuning of Large Language Models (LLMs) on financial data for sentiment classification holds great potential. This comprehensive guide will seamlessly transition from exploring RAG-enhanced LLMs to detailing the step-by-step process of fine-tuning on limited hardware. The aim is to empower you with the skills to harness the capabilities of natural language processing in the dynamic and specialized field of finance.

In this tutorial, we delve into the fine-tuning of the Llama 2 model on financial data for sentiment classification using the LoRA approach. Various methods have been proposed for refining extensive language models, and LoRA (Low-Rank Adaptation of Large Language Models)…