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
https://huggingface.co/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:
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.
Installation
# Clone the repository
git clone https://github.com/AI4Finance-Foundation/FinGPT.git
cd FinGPT
# Install dependencies
pip install -r requirements.txt
pip install -e .
Try the Demo
Visit our HuggingFace Space to try FinGPT-Forecaster without any installation!
Basic Usage
# Using cloud API (no GPU required)
import os
os.environ['FINGPT_LLM_PROVIDER'] = 'openai'
# Using local models (requires GPU)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# See SETUP.md for detailed examples
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 alternative. It prioritizes lightweight adaptation, leveraging the best available open-source LLMs.
3). The key technology is "RLHF (Reinforcement learning from human feedback)", which is missing in BloombergGPT. RLHF enables an LLM model to learn individual preferences (risk-aversion level, investing habits, personalized robo-advisor, etc.), which is the "secret" ingredient of ChatGPT and GPT4.
Milestone of AI Robo-Advisor: FinGPT-Forecaster
Try the latest released FinGPT-Forecaster demo at our HuggingFace Space
The dataset for FinGPT-Forecaster: https://huggingface.co/datasets/FinGPT/fingpt-forecaster-dow30-202305-202405
Enter the following inputs:
- ticker symbol (e.g. AAPL, MSFT, NVDA)
- the day from which you want the prediction to happen (yyyy-mm-dd)
- the number of past weeks where market news are retrieved
- whether to add the latest basic financials as additional information
Click Submit！ And you'll be responded with a well-rounded analysis of the company and a prediction for next week's stock price movement!
For detailed and more customized implementation, please refer to FinGPT-Forecaster
FinGPT Demos:
Current State-of-the-arts for Financial Sentiment Analysis
Instruction Tuning Datasets and Models
The datasets we used, and the multi-task financial LLM models are available at https://huggingface.co/FinGPT
Our Code
| Datasets |
Train Rows |
Test Rows |
Description |
| fingpt-sentiment-train |
76.8K |
N/A |
Sentiment Analysis Training Instructions |
| fingpt-finred |
27.6k |
5.11k |
Financial Relation Extraction Instructions |
| fingpt-headline |
82.2k |
20.5k |
Financial Headline Analysis Instructions |
| fingpt-ner |
511 |
98 |
Financial Named-Entity Recognition Instructions |
| fingpt-fiqa_qa |
17.1k |
N/A |
Financial Q&A Instructions |
| fingpt-fineval |
1.06k |
265 |
Chinese Multiple-Choice Questions Instructions |
Multi-task financial LLMs Models:
demo_tasks = [
'Financial Sentiment Analysis',
'Financial Relation Extraction',
'Financial Headline Classification',
'Financial Named Entity Recognition',]
demo_inputs = [
"Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano",
"Apple Inc. Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.",
'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600',
'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (" Bank "), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',]
demo_instructions = [
'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.',
'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be "relation1: word1, word2; relation2: word3, word4". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.',
'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.',
'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',]
Tutorials
[Training] Beginner’s Guide to FinGPT: Training with LoRA and ChatGLM2–6B One Notebook, $10 GPU
Understanding FinGPT: An Educational Blog Series
FinGPT Ecosystem
FinGPT embraces a full-stack framework for FinLLMs with five layers:
- Data source layer: This layer assures comprehensive market coverage, addressing the temporal sensitivity of financial data through real-time information capture.
- Data engineering layer: Primed for real-time NLP data processing, this layer tackles the inherent challenges of high temporal sensitivity and low signal-to-noise ratio in financial data.
- LLMs layer: Focusing on a range of fine-tuning methodologies such as LoRA, this layer mitigates the highly dynamic nature of financial data, ensuring the model’s relevance and accuracy.
- Task layer: This layer is responsible for executing fundamental tasks. These tasks serve as the benchmarks for performance evaluations and cross-comparisons in the realm of FinLLMs
- Application layer: Showcasing practical applications and demos, this layer highlights the potential capability of FinGPT in the financial sector.
- FinGPT Framework: Open-Source Financial Large Language Models
- FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions.
- FinGPT-FinNLP: FinNLP provides a playground for all people interested in LLMs and NLP in Finance. Here we provide full pipelines for LLM training and finetuning in the field of finance. The full architecture is shown in the following picture. Detail codes and introductions can be found here. Or you may refer to the wiki
- FinGPT-Benchmark: We introduce a novel Instruction Tuning paradigm optimized for open-source Large Language Models (LLMs) in finance, enhancing their adaptability to diverse financial datasets while also facilitating cost-effective, systematic benchmarking from task-specific, multi-task, and zero-shot instruction tuning tasks.
Cloud LLM Providers for FinGPT Inference
For the Finogrid platform (finogrid/), FinGPT supports multiple cloud LLM providers as alternatives to running local models. Set FINGPT_LLM_PROVIDER in your environment:
| Provider |
Model |
Context Length |
Use Case |
| OpenAI |
GPT-3.5-turbo |
16K |
Default fallback for sentiment & agents |
| MiniMax |
MiniMax-M3 |
512K |
Latest flagship model with 128K max output and image input support |
| Fin |
|
|
|
FinGPT 是 AI4Finance Foundation 的金融大语言模型开源项目：训练好的模型发布在 HuggingFace 上，并附带围绕这些模型的 RAG、微调流水线和基准测试代码。