fingpt-complete

FinGPT: Open-Source Financial Large Language Models

<div align="center"> <img src="https://github.com/AI4Finance-Foundation/FinGPT/assets/31713746/e0371951-1ce1-488e-aa25-0992dafcc139" width="30%"> </div>

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

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:

1. Data Source Layer: Comprehensive market coverage with real-time information
2. Data Engineering Layer: Real-time NLP data processing
3. LLMs Layer: Fine-tuning methodologies (LoRA, QLoRA)
4. Task Layer: Fundamental financial tasks and benchmarks
5. 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

• GitHub Repository
• Discord Community
• AI4Finance Website

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<div align="center"> <strong>FinGPT: Democratizing Financial AI for Everyone</strong> </div>