Instruction Fine-Tuning LLM using SFT for Financial Sentiment: A Step-by-Step Guide

Author(s): Youssef Hosni

Originally published on Towards AI.

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Instruction fine-tuning allows large language models (LLMs) to be adapted for specific tasks by guiding them with clear, task-oriented instructions. In this article, we focus on fine-tuning the Facebook/opt-1.3b model for financial sentiment analysis using Supervised Fine-Tuning (SFT).

The process begins with setting up the environment and loading a financial dataset from Deep Lake, followed by the initialization of the model and training configuration.

We also explore how LoRA (Low-Rank Adaptation) can be combined with the OPT model to make the fine-tuning process more efficient. Finally, we demonstrate how to run inference, applying the fine-tuned model to real-world financial data.

This guide will be helpful for machine learning practitioners, data scientists, and developers who want to fine-tune large language models for domain-specific applications, especially in finance. Whether you’re new to model fine-tuning or looking for a hands-on approach to adapting models for sentiment analysis, this article covers the essential steps.

Setting-Up Working EnvironmentLoad the Deep Lake DatasetInitialize the Model and TrainerFine Tuning LLM with SFTMerging LoRA and OPTInference

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Published via Towards AI