AI4Finance-Foundation / FinGPT

# FinGPT: Open-Source Financial Large Language Models
Let us not expect Wall Street to open-source LLMs or open APIs, due to FinTech institutes' internal regulations and policies.
Blueprint of FinGPT
## Project Contributors
FinGPT is an open-source financial large language model project developed and maintained by the AI4Finance Foundation.
Key contributors include:
- **Hongyang (Bruce) Yang** – research and development on financial large language models and related applications
- [other contributors…]
## What's New:
 - [Model Release] Nov, 2023: We release FinGPT-Forecaster! 🔥Demo, Medium Blog & Model are available on Huggingface🤗!
 - [Paper Acceptance] Oct, 2023: "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets" is accepted🎉 by Instruction Workshop @ NeurIPS 2023 
 - [Paper Acceptance] Oct, 2023: "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models" is accepted🎉 by Instruction Workshop @ NeurIPS 2023
 - [Model Release] Oct, 2023: We release the financial multi-task LLMs 🔥 produced when evaluating base-LLMs on FinGPT-Benchmark
 - [Paper Acceptance] Sep, 2023: "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" is accepted🎉 by ACM International Conference on AI in Finance (ICAIF-23)
 - [Model Release] Aug, 2023: We release the financial sentiment analysis model 🔥
 - [Paper Acceptance] Jul, 2023: "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models" is accepted🎉 by FinLLM 2023@IJCAI 2023
 - [Paper Acceptance] Jul, 2023: "FinGPT: Open-Source Financial Large Language Models" is accepted🎉 by FinLLM 2023@IJCAI 2023
 - [Medium Blog] Jun 2023: FinGPT: Powering the Future of Finance with 20 Cutting-Edge Applications
## Quick Start
**🚀 Get started with FinGPT in minutes!**
For detailed setup instructions for running FinGPT locally or on Replit, including hardware requirements and troubleshooting, check out our comprehensive SETUP Guide.
**❓ Have questions?** Check our FAQ for answers to common questions about FinGPT usage, capabilities, and limitations.
### Installation
 [code] 
### Try the Demo
Visit our HuggingFace Space to try FinGPT-Forecaster without any installation!
### Basic Usage
 [code] 
## Why FinGPT?
1). Finance is highly dynamic. BloombergGPT trained an LLM using a mixture of finance data and general-purpose data, which took about 53 days, at a cost of around **$3M**). It is costly to retrain an LLM model like BloombergGPT every month or every week, thus lightweight adaptation is highly favorable. FinGPT can be fine-tuned swiftly to incorporate new data (the cost falls significantly, less than **$300 per fine-tuning**).
2). Democratizing Internet-scale financial data is critical, say allowing timely updates of the model (monthly or weekly updates) using an automatic data curation pipeline. BloombergGPT has privileged data access and APIs, while FinGPT presents a more accessible