FinGPT: Open-Source Financial LLMs
Open-source large language model framework for financial NLP using data-centric approach with automatic data curation and low-rank adaptation (LoRA) for efficient fine-tuning.
Verdict
Framework code released under the MIT license for fine-tuning LLMs on financial text with LoRA. Released adapters cover sentiment, multi-task and forecasting and use base models including Llama 2 and ChatGLM2; the licenses for those base models also apply. In the README's weighted-F1 table for FPB, FiQA-SA and TFNS, the authors report higher scores for their Llama2-13B sentiment adapter (FinGPT v3.3) than for GPT-4 on all three.
Practical Use
Best for financial sentiment analysis, text classification, and basic financial question answering where you need a customizable, open-source solution. The lightweight LoRA fine-tuning approach makes it practical to adapt to specific financial domains (earnings calls, news, social media) without massive compute. Released models on Hugging Face include sentiment classifiers and forecasting variants. Limitations: The released adapters target defined tasks, including sentiment, relation extraction, headline classification and named-entity recognition. FinGPT-Forecaster is trained on US market data from the DOW 30. Review the applicable licenses before commercial use.
Paper
https://arxiv.org/abs/2306.06031Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang. FinLLM Workshop @ IJCAI, 2023
Code Repository
https://github.com/AI4Finance-Foundation/FinGPTLicense
Custom. Code (AI4Finance-Foundation/FinGPT): MIT. Paper: arXiv non-exclusive distribution license (free to read; no reuse license).
Installation
git clone https://github.com/AI4Finance-Foundation/FinGPT