FinGPT provides a suite of open-source Large Language Models (LLMs) specifically designed for the financial domain, addressing the high cost and proprietary nature of commercial financial LLMs like BloombergGPT. It offers lightweight adaptation and democratizes access to financial data for researchers and practitioners.

How It Works

FinGPT leverages instruction tuning and fine-tuning techniques (including LoRA and QLoRA) on various open-source base LLMs (e.g., Llama-2, Falcon, ChatGLM2) using curated financial datasets. This approach allows for rapid, cost-effective adaptation to new financial data and enables personalized financial applications through techniques like RLHF, aiming to match or exceed the performance of proprietary models.

Quick Start & Requirements

• Install: Primarily through HuggingFace models and datasets. Code can be cloned from the repository.
• Prerequisites: Python, PyTorch, HuggingFace libraries. Specific models may require GPUs (e.g., RTX 3090 for FinGPT v3.3).
• Resources: Fine-tuning can be achieved on a single RTX 3090 (e.g., ~$17 for FinGPT v3.3).
• Links: HuggingFace Models, Demo, Tutorials.

Highlighted Details

• FinGPT v3.3 (Llama2-13b) achieves a weighted F1 of 0.882 on the FPB benchmark, outperforming GPT-4 on several financial sentiment analysis datasets.
• Offers models for multi-task financial NLP (sentiment analysis, relation extraction, NER) and specialized tasks like stock forecasting.
• Provides a framework for retrieval-augmented generation (FinGPT-RAG) for enhanced financial sentiment analysis.
• Includes a benchmark suite (FinGPT-Benchmark) for systematic evaluation of LLMs on financial datasets.

Maintenance & Community

• Active development with regular model releases and paper acceptances at major AI conferences.
• Community engagement is encouraged for contributing base models and improving datasets.
• GitHub Repository

Licensing & Compatibility

• License: MIT License for code. Models are typically released under licenses compatible with their base models (e.g., Llama-2 license).
• Compatibility: MIT license permits commercial use and linking with closed-source projects.

Limitations & Caveats

• The project is primarily for academic and research purposes; users are cautioned that it is not financial advice. Performance can vary based on the chosen base model and fine-tuning data.