FinGPT: Open-Source Financial Large Language Models

Model Description

FinGPT is an open-source financial large language model that revolutionizes the financial industry by providing accessible, lightweight, and cost-effective solutions for financial tasks. Unlike proprietary models like BloombergGPT, FinGPT democratizes financial AI by offering:

Lightweight Adaptation: Fine-tuning costs less than $300 vs $3M for BloombergGPT
Real-time Updates: Monthly/weekly model updates through automatic data curation
RLHF Integration: Reinforcement Learning from Human Feedback for personalized preferences
Multi-language Support: English and Chinese market data processing

Key Features

State-of-the-Art Performance

FinGPT v3.3: Best trainable and inferable model for sentiment analysis on single RTX 3090
Superior to GPT-4: Outperforms GPT-4 and ChatGPT fine-tuning in financial tasks
Cost-Effective: 17.25 hours training on RTX 3090 for $17.25

Comprehensive Benchmark Results

Model	FPB	FiQA-SA	TFNS	NWGI	Device	Time	Cost
FinGPT v3.3	0.882	0.874	0.903	0.643	RTX 3090	17.25h	$17.25
GPT-4	0.833	0.630	0.808	-	-	-	-
BloombergGPT	0.511	0.751	-	-	512×A100	53 days	$2.67M

Multi-Task Capabilities

Financial Sentiment Analysis
Financial Relation Extraction
Financial Headline Classification
Financial Named Entity Recognition
Financial Q&A
Robo-Advisor Services

Model Architecture

FinGPT embraces a full-stack framework with five layers:

Data Source Layer: Comprehensive market coverage with real-time information
Data Engineering Layer: Real-time NLP data processing
LLMs Layer: Fine-tuning methodologies (LoRA, QLoRA)
Task Layer: Fundamental financial tasks and benchmarks
Application Layer: Practical applications and demos

Available Models

Multi-Task Models

fingpt-mt_llama2-7b_lora: Fine-tuned Llama2-7b with LoRA
fingpt-mt_falcon-7b_lora: Fine-tuned Falcon-7b with LoRA
fingpt-mt_chatglm2-6b_lora: Fine-tuned ChatGLM2-6b with LoRA

Specialized Models

fingpt-sentiment_llama2-13b_lora: Financial sentiment analysis
fingpt-forecaster_dow30_llama2-7b_lora: Stock price forecasting

Quick Start

Installation

pip install fingpt

Basic Usage

from fingpt import FinGPT

# Initialize model
model = FinGPT.from_pretrained("FinGPT/fingpt-sentiment_llama2-13b_lora")

# Financial sentiment analysis
text = "Apple Inc. reported strong quarterly earnings, beating analyst expectations."
result = model.analyze_sentiment(text)
print(result)  # Output: positive

Citation

@article{yang2023fingpt,
  title={FinGPT: Open-Source Financial Large Language Models},
  author={Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan},
  journal={FinLLM Symposium at IJCAI 2023},
  year={2023}
}

License

MIT License

Disclaimer

This model is for academic and research purposes only. Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always consult a professional before trading or investing.

Community

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